Kaldi - Detailed Review
Analytics Tools
Kaldi - Product Overview
Introduction to Kaldi
Kaldi is an open-source speech recognition toolkit that plays a significant role in the Analytics Tools AI-driven product category, particularly in automatic speech recognition (ASR).
Primary Function
Kaldi’s primary function is to provide a flexible and extensible platform for building and researching speech recognition systems. It is used for speech recognition and signal processing, allowing users to develop high-performing ASR systems.
Target Audience
The target audience for Kaldi includes ASR researchers, developers, and industry professionals. It is particularly useful for those in academic disciplines and industrial sectors who need to develop and deploy advanced speech recognition solutions.
Key Features
- Flexibility and Extensibility: Kaldi is written in C and is highly customizable, making it suitable for a wide range of speech recognition tasks.
- Acoustic Feature Generation: It can generate various acoustic features such as MFCC, fbank, and fMLLR, which are essential for pre-processing raw waveform data for deep neural network models.
- Training Techniques: Kaldi supports several training techniques including linear transforms, MMI, boosted MMI, MCE discriminative training, and deep neural networks.
- Real-Time Decoding: The toolkit includes support for online (real-time) decoding, which is crucial for applications requiring immediate speech-to-text conversion.
- Voice Activity Detection and Decoders: Recent enhancements include improved voice activity detection and a faster decoder, as well as support for recurrent neural net language models and acoustic models.
- Cross-Platform Compatibility: Kaldi is available for Unix systems, including Linux, BSD, and OSX, as well as Windows via Cygwin.
Applications
Kaldi’s applications span across various domains, including voice assistants, transcription services, call center automation, language learning platforms, accessibility tools, voice-controlled devices, healthcare documentation, and broadcasting and media.
By providing these features and capabilities, Kaldi has become the most widely used open-source toolkit for ASR research, supporting hundreds of researchers and developers globally.
Kaldi - User Interface and Experience
User Interface and Experience in Kaldi
Command-Line Interface
Kaldi is primarily operated through command-line scripts and configuration files. Users interact with it by writing and executing shell scripts, such as `cmd.sh`, `path.sh`, and `run.sh`, which are essential for setting up and running ASR systems.Script-Based Workflow
The workflow involves creating and modifying various scripts to configure the ASR system. For example, users need to prepare scripts in directories like `egs` to build ASR systems for specific speech corpora. This requires a good understanding of shell scripting and the Kaldi toolkit’s structure.Technical Expertise Required
Kaldi is geared more towards researchers and developers in the field of speech recognition rather than end-users. It demands a significant amount of technical expertise, particularly in C , shell scripting, and speech recognition concepts. The learning curve is quite steep, and users need to be comfortable with command-line operations and scripting.Documentation and Community Support
While Kaldi lacks a user-friendly interface, it is well-documented with extensive tutorials, README files, and community support. The official website and various tutorials provide step-by-step guides to help users get started and troubleshoot issues.Customization and Flexibility
One of the strengths of Kaldi is its flexibility and customizability. Users can configure various parameters, such as feature extraction, acoustic modeling, and language modeling, to suit their specific needs. However, this flexibility comes at the cost of increased complexity and the need for detailed configuration.No GUI for End-Users
Unlike some other AI-driven products, Kaldi does not offer a graphical user interface (GUI) for end-users. It is not intended for casual use but rather for advanced research and development in speech recognition.Conclusion
In summary, Kaldi’s user interface is command-line based, requiring technical expertise and a willingness to work with scripts and configuration files. While it offers great flexibility and customization options, it is not designed for ease of use by non-technical end-users. Kaldi - Key Features and Functionality
Kaldi Overview
Kaldi is a powerful and flexible open-source toolkit specifically designed for building automatic speech recognition (ASR) systems. Here are the main features and how they work, along with the integration of AI:Feature Extraction
Kaldi supports various feature extraction techniques, which are crucial for capturing the acoustic properties of speech. Key features include:Mel-frequency cepstral coefficients (MFCCs)
These are widely used in speech recognition to represent the short-term power spectrum of speech.Filter banks
These help in extracting features that are similar to the human auditory system.fMLLR (feature-space Maximum Likelihood Linear Regression)
This is used for speaker adaptation and improving recognition accuracy. These features are extracted using scripts and tools provided by Kaldi, such as `steps/make_mfcc.sh`, which generates MFCC features for the training data.Acoustic Modeling
Kaldi offers a range of acoustic models to predict the likelihood of phonetic units given the extracted features:Gaussian Mixture Models (GMMs)
These are used to characterize the distribution of acoustic features. GMMs are combined with Hidden Markov Models (HMMs) to model the temporal variability of speech.Deep Neural Networks (DNNs)
Kaldi supports various neural network architectures, including feed-forward networks, recurrent networks, and convolutional networks. These models can be trained using Kaldi’s training scripts and are particularly effective in modern ASR systems.Language Modeling
Language models in Kaldi help predict the likelihood of word sequences:N-gram models
These statistical models predict the probability of a word given the context of the previous words.Neural network-based models
Kaldi also supports neural network-based language models, which can be integrated with the acoustic models to improve recognition accuracy.Decoding
The decoding process in Kaldi combines the outputs of the acoustic and language models to produce the final transcription:Weighted Finite-State Transducer (WFST) decoder
This decoder uses a weighted finite-state transducer to search for the most likely sequence of words given the predicted phonetic units and language model constraints. The Viterbi algorithm is used to find the most likely sequence efficiently.Training and Evaluation
Kaldi provides comprehensive tools for training and evaluating ASR models:Training scripts
Scripts like `steps/train_ctc.sh` and `steps/train_deltas.sh` allow users to train different types of models, including end-to-end models and traditional GMM-HMM models.Evaluation tools
Kaldi includes tools to evaluate the performance of the models using metrics such as word error rate (WER).Extensibility and Customization
Kaldi is highly extensible and customizable:Modular architecture
Users can pick and choose the components they need, allowing for a high degree of customization.Integration with other frameworks
Kaldi can be integrated with other deep learning frameworks like TensorFlow, enabling the use of various neural network architectures.AI Integration
Kaldi heavily leverages AI and machine learning techniques:Deep neural networks
The use of DNNs in acoustic modeling and language modeling significantly improves the accuracy of speech recognition.End-to-end models
Kaldi supports end-to-end ASR models that directly transcribe speech into text without intermediate alignments, using techniques like Connectionist Temporal Classification (CTC) and attention-based models. Overall, Kaldi’s integration of AI through deep neural networks, advanced feature extraction, and efficient decoding algorithms makes it a powerful and versatile toolkit for building state-of-the-art ASR systems. Kaldi - Performance and Accuracy
Performance
Kaldi’s performance is bolstered by its comprehensive set of tools and components. It starts with robust feature extraction, transforming raw audio signals into meaningful representations such as Mel Frequency Cepstral Coefficients (MFCCs), filter banks, and pitch features. These features are then fed into acoustic models, which can be based on Gaussian Mixture Models (GMMs), Deep Neural Networks (DNNs), or Recurrent Neural Networks (RNNs), among others. The use of DNNs and RNNs has significantly improved the performance of Kaldi-based ASR systems, especially in capturing temporal dependencies and complex relationships in speech data.Accuracy
The accuracy of Kaldi is enhanced by its weighted finite state transducer (WFST) based decoder, which searches for the most likely sequence of words given the predicted phonetic units and language model constraints. This combination allows Kaldi to achieve high recognition accuracy in various speech recognition tasks. For instance, Kaldi’s ability to handle noisy environments and its extensive tuning capabilities often result in lower Word Error Rates (WER) compared to other systems like DeepSpeech, particularly in challenging audio conditions.Limitations and Areas for Improvement
Flexibility in DNN Models
One of the main limitations of Kaldi is its limited flexibility in implementing new DNN models. This rigidity can make it less adaptable to the latest deep learning architectures compared to more flexible frameworks like PyTorch and TensorFlow. To address this, researchers have developed integrations such as the PyTorch-Kaldi project and the Pkwrap project, which allow users to leverage the flexibility of these frameworks while still benefiting from Kaldi’s efficient decoding capabilities.Optimization for Embedded Devices
Another area of focus is the optimization of Kaldi for embedded devices. Research has been conducted on parameter quantization to reduce the number of parameters required for DNN-based acoustic models, making them more suitable for operation on resource-constrained devices.Practical Applications and Benchmarks
Kaldi’s performance and accuracy are evident in its practical applications, such as voice assistants, transcription services, and real-time speech-to-text conversion. Companies like ExKaldi-RT have successfully utilized Kaldi to develop online ASR toolkits that achieve competitive performance in real-time applications.Benchmark Performance
In terms of benchmarks, Kaldi often performs well in traditional speech recognition tasks, but the speed at which benchmark saturation is being reached is increasing. New benchmarking suites are being developed to provide more comprehensive evaluations, which will help in further improving Kaldi’s performance.Conclusion
In summary, Kaldi offers high performance and accuracy in speech recognition, particularly due to its advanced feature extraction, versatile acoustic modeling, and efficient decoding capabilities. However, it faces challenges in terms of flexibility with new DNN models, which are being addressed through integrations with other deep learning frameworks. Kaldi - Pricing and Plans
Pricing Structure of Kaldi
The Kaldi speech recognition toolkit does not have a pricing structure in the traditional sense, as it is an open-source project. Here are the key points regarding its availability and use:Open-Source Nature
Kaldi is completely free and open-source, making it accessible to anyone without any cost. It is available for download and use on various platforms, including Unix-like systems and Microsoft Windows.No Subscription or Licensing Fees
There are no subscription fees, licensing costs, or tiered plans associated with using Kaldi. Users can download, modify, and extend the code without any financial obligations.Community Support and Resources
Kaldi is supported by a community of developers and researchers. It comes with extensive documentation, example scripts, and tools that help users set up and run their own speech recognition systems. The official website and associated resources provide step-by-step tutorials and guides for beginners.Integration with Other Systems
While Kaldi itself is free, some integrations or plugins that use Kaldi might have associated costs. For example, integrating Kaldi with the UniMRCP Server through a plugin may involve setup and support fees, but these are not part of the Kaldi project itself.Conclusion
In summary, Kaldi is a free, open-source toolkit with no pricing structure or tiers, making it freely available for anyone to use and contribute to. Kaldi - Integration and Compatibility
Kaldi Overview
Kaldi, an open-source speech recognition toolkit, is highly versatile and integrates well with various tools and platforms, making it a valuable asset in the development of AI-driven products.Platform Compatibility
Kaldi is written in C and is compatible with both CPU and GPU environments. It offers two sets of images: CPU-based and GPU-based, which allows for flexible deployment depending on the computational resources available.Integration with Other Tools
Kaldi can be integrated with several other tools and frameworks:- Python Wrappers: There are several Python wrappers available for Kaldi, which facilitate its use in Python-based projects. This integration is particularly useful for developers who prefer working in Python.
- PyTorch: Although Kaldi traditionally does not use PyTorch or TensorFlow due to historical reasons, there are plans to integrate Kaldi with PyTorch. A PyTorch-integrated version of Kaldi is in the planning stage, which will enhance its compatibility with deep learning frameworks.
- OpenFst: Kaldi uses OpenFst for finite-state transducers, which is a freely available toolkit. This integration is crucial for building complete recognition systems.
Cross-Platform Support
Kaldi is not limited to a specific operating system:- Windows: While Kaldi itself is not natively optimized for Windows, there are third-party tools like VoiceBridge that provide a Windows-compatible version based on Kaldi. This makes it accessible to Windows developers as well.
- Linux and Other Unix-like Systems: Kaldi is primarily developed and tested on Linux and other Unix-like systems, where it runs seamlessly.
Advanced Use Cases
For advanced speech recognition tasks, such as audio-visual speech recognition, Kaldi can be extended using additional scripts and models. For example, the baseline system for audio-visual speech recognition using Kaldi integrates deep neural networks with dynamic stream reliability estimates, showcasing its adaptability to complex recognition tasks.Future Enhancements
The next-generation Kaldi tools, such as those found in the K2-FSA project, offer advanced features like fast training with pruned RNN-T loss, zipformer models, and multi-quantization utilities. These enhancements are designed to be easy to use and support multiple platforms, further expanding Kaldi’s integration capabilities.Conclusion
In summary, Kaldi’s flexibility in integration and its compatibility across different platforms make it a highly adaptable and valuable toolkit for speech recognition research and development. Kaldi - Customer Support and Resources
Documentation and Tutorials
Kaldi provides comprehensive documentation and tutorials to help users get started. The official Kaldi tutorial is a detailed guide that covers prerequisites, getting started, version control, and running example scripts.
Additionally, there is a “Kaldi for Dummies” tutorial, which is a step-by-step guide for absolute beginners. This tutorial helps users install Kaldi, set up an ASR system using their own audio data, and run the system to get their first speech decoding results.
Community and Resources
The Kaldi community is active and supportive. The “Awesome Kaldi” repository on GitHub is a valuable resource that lists various features, scripts, blogs, and projects related to Kaldi. This includes production-ready examples, resources for understanding the math and science behind Kaldi, and other useful tools and implementations.
Example Scripts and Projects
Kaldi comes with several example scripts and projects that can help users learn how to implement ASR systems. For instance, the egs directory contains example scripts for building ASR systems for over 30 popular speech corpora. These examples are well-documented and can serve as a starting point for custom projects.
Integration with Other Tools
Kaldi can be integrated with other frameworks and tools to enhance its functionality. For example, there are projects like kaldi-gstreamer-server that help integrate Kaldi with the GStreamer framework, and kaldi-offline-transcriber which handles both training and decoding for various languages.
PyKaldi
For users who prefer working with Python, PyKaldi provides an easy-to-use API for Kaldi’s speech recognition capabilities. This module includes classes like Recognizer, LatticeFasterRecognizer, and MappedRecognizer, which simplify the process of decoding speech inputs.
Data Preparation and Quality
Kaldi emphasizes the importance of high-quality data for effective ASR. Resources are available to guide users on data preparation, cleaning, and labeling. Ensuring data quality is crucial for achieving accurate results in speech recognition.
While Kaldi does not offer traditional customer support like a dedicated help desk or phone support, the extensive documentation, community resources, and example projects make it a well-supported toolkit for those interested in ASR.
Kaldi - Pros and Cons
Advantages of Kaldi
Flexibility and Modern Code
Kaldi is praised for its modern, flexible, and cleanly structured code, making it easier to understand, modify, and extend compared to other toolkits like HTK and the RWTH ASR toolkit.
Open License
Kaldi is released under the Apache License v2.0, which is highly non-restrictive, allowing for widespread use and modification by a broad community of users.
Comprehensive Feature Support
Kaldi supports a wide range of features, including finite-state transducers (FSTs), subspace Gaussian mixture models (SGMMs), and standard Gaussian mixture models. It also integrates well with linear and affine transforms.
Deep Learning Integration
Kaldi can be integrated with other deep learning frameworks such as PyTorch and TensorFlow, enabling the use of various neural network architectures and improving its flexibility and performance in speech recognition tasks.
Efficient Feature Extraction
Kaldi has been optimized for batched online feature extraction, which improves latency and throughput, especially when processing real-time audio from multiple sources. This makes it more practical for applications requiring immediate transcription.
Community and Documentation
Kaldi benefits from detailed documentation and a supportive community, which is crucial for researchers and developers working on speech recognition projects. It also includes scripts for building complete recognition systems.
Practical Applications
Kaldi is used in various practical applications, such as voice assistants, transcription services, and real-time speech-to-text conversion, demonstrating its versatility and effectiveness in real-world scenarios.
Disadvantages of Kaldi
Limited Flexibility in New DNN Models
One of the challenges with Kaldi is its limited flexibility when implementing new deep neural network (DNN) models. This requires additional efforts and integrations with other frameworks like PyTorch and TensorFlow to overcome.
Steep Learning Curve
While Kaldi is well-documented, its comprehensive set of tools and components can make it challenging for new users to learn and master, especially those without a strong background in speech recognition and machine learning.
Dependency on Additional Frameworks
To fully leverage the latest advancements in deep learning, Kaldi often needs to be integrated with other frameworks. This can add complexity to the development process and require additional expertise.
Performance Optimization
Optimizing Kaldi for specific use cases, such as reducing the number of parameters for DNN-based acoustic models to operate on embedded devices, can be a complex task and may require significant research and development efforts.
In summary, Kaldi offers significant advantages in terms of flexibility, open licensing, and comprehensive feature support, but it also presents some challenges, particularly in implementing new DNN models and requiring additional integrations and expertise.
Kaldi - Comparison with Competitors
When Comparing Kaldi with Other AI-Driven Speech Recognition Tools
Several key aspects and alternatives come into focus.
Unique Features of Kaldi
- Open-Source and Flexible: Kaldi is an open-source toolkit, making it highly flexible and extensible. It is widely used in both academic and industrial settings due to its open-source nature and extensive documentation.
- Core Components: Kaldi includes feature extraction tools, acoustic modeling techniques such as Gaussian Mixture Models (GMMs) and Deep Neural Networks (DNNs), and language modeling capabilities. These components are crucial for developing comprehensive speech recognition systems.
- Multi-Stage Training: Kaldi employs a multi-stage training strategy that includes data preparation, feature extraction, model training, decoding, and evaluation. This approach enhances the performance of language models and the overall accuracy of the recognition system.
Alternatives and Comparisons
Whisper by OpenAI
- Accuracy and Multilingual Support: Whisper is known for its high accuracy and support for nearly 100 languages. It approaches human-level robustness and accuracy on English speech recognition and offers translation capabilities. However, it is significantly slower than some other models like wav2vec 2.0.
- Usability: Whisper is generally easier to set up and use compared to Kaldi, especially for developers without extensive expertise in speech recognition.
wav2vec 2.0 by Facebook
- Performance and Speed: wav2vec 2.0 offers better accuracy than Kaldi but is outperformed by Whisper. It is, however, much faster than Whisper, making it a good choice for real-time transcription needs.
- Usability: Like Whisper, wav2vec 2.0 is more user-friendly and quicker to implement than Kaldi, particularly for those familiar with deep learning frameworks.
Other Open-Source Models
- wav2letter and VOSK: These models are also part of the open-source speech recognition ecosystem. wav2letter is another end-to-end model, while VOSK is built on Kaldi foundations and offers both open-source and commercial models. These alternatives may offer different trade-offs in terms of accuracy, speed, and usability depending on the specific use case.
Potential Use Cases and Considerations
- Academic and Industrial Use: Kaldi is highly valued in academic and industrial settings due to its flexibility and the ability to customize and extend its components. However, for simpler use cases or those requiring quick deployment, models like Whisper or wav2vec 2.0 might be more suitable.
- Real-Time Transcription: If real-time transcription is a requirement, wav2vec 2.0 or other faster models might be preferable. For applications where accuracy is paramount and speed is less critical, Whisper could be the better choice.
In summary, while Kaldi offers a highly customizable and flexible framework for speech recognition, it may require more technical expertise to set up and optimize. Alternatives like Whisper and wav2vec 2.0 provide easier-to-use solutions with different strengths in terms of accuracy and speed, making them viable options depending on the specific needs of the project.
Kaldi - Frequently Asked Questions
1. How do I get started with Kaldi?
To get started with Kaldi, you need to download the toolkit from the official Kaldi GitHub repository. Follow the installation instructions provided in the documentation. After installation, prepare your dataset by organizing audio files and their corresponding transcriptions. Kaldi includes scripts to assist with data formatting and preprocessing. You can then use the provided recipes to train your ASR models.2. What are the key features of Kaldi?
Kaldi supports various key features, including feature extraction (e.g., MFCCs, filter banks), acoustic modeling (GMMs, deep neural networks), and language modeling (n-gram models, neural network-based approaches). It also provides tools for training end-to-end (E2E) ASR models, decoding, and customizing different aspects of the system.3. Can Kaldi run on AMD GPUs? Is an OpenCL port available?
Currently, there is no OpenCL port available for Kaldi to run on AMD GPUs. Kaldi’s GPU support is primarily optimized for NVIDIA GPUs. However, you can check the latest updates and discussions on the Kaldi mailing lists or forums for any potential developments.4. How do I train an end-to-end (E2E) ASR model in Kaldi?
To train an E2E ASR model in Kaldi, start by collecting and preprocessing your audio data, ensuring it is properly labeled with corresponding transcripts. Use Kaldi’s feature extraction tools to convert the audio into suitable features (e.g., MFCCs, filter bank features). Then, use Kaldi’s training scripts to train your E2E model, choosing between CTC or attention-based models depending on your needs. Finally, evaluate your model’s performance on a validation set using Kaldi’s scoring tools.5. What is the difference between MFCC and filter bank features in Kaldi?
MFCC (Mel-frequency cepstral coefficients) and filter bank features are both used for capturing the acoustic properties of speech. MFCCs are derived from the Mel-frequency cepstral coefficients, which are based on the human auditory system, while filter bank features are derived from a set of triangular filters spaced on the Mel scale. Both are widely used in speech recognition, but MFCCs are more traditional and commonly used, especially in conjunction with deep neural networks.6. How can I optimize model training in Kaldi?
To optimize model training in Kaldi, you can adjust various hyperparameters such as the learning rate, batch size, and the number of epochs. Utilize scripts like `steps/train_mono.sh` for monophone models and `steps/train_deltas.sh` for triphone models. Additionally, you can customize feature extraction parameters and use different decoding strategies to enhance recognition performance.7. What is the role of the language model in Kaldi?
The language model in Kaldi predicts the likelihood of word sequences, which is crucial for enhancing the accuracy of the transcription. Kaldi supports both statistical (n-gram models) and neural network-based language models. The language model works in conjunction with the acoustic model and decoder to produce the final transcription.8. How can I handle silence modeling during training and testing in Kaldi?
To remove or handle silence modeling during training and testing, you can adjust the configuration files and scripts provided by Kaldi. For example, you can modify the lexicon and the decoding graphs to exclude silence models or adjust the silence modeling parameters in the training recipes.9. What is the significance of i-vectors in Kaldi?
I-vectors (identity vectors) in Kaldi are used for speaker adaptation and diarization. They help in capturing speaker-specific characteristics, which can improve the performance of the acoustic models, especially in multi-speaker environments. I-vectors are particularly useful when you need to adapt the models to new speakers or environments.10. How can I evaluate the performance of my ASR model in Kaldi?
To evaluate the performance of your ASR model in Kaldi, use the scoring tools provided to measure metrics such as the Word Error Rate (WER) and Sentence Error Rate (SER). These metrics help in assessing the accuracy of the transcription output. You can run these evaluations on a validation set to get a reliable measure of your model’s performance. Kaldi - Conclusion and Recommendation
Final Assessment of Kaldi in the Analytics Tools AI-Driven Product Category
Kaldi is a highly regarded, open-source toolkit for speech recognition research and development, making it a valuable asset in the analytics tools AI-driven product category.
Key Benefits and Features
- Modular and Flexible Design: Kaldi’s architecture is highly modular, allowing users to easily experiment with different model architectures and training techniques. This flexibility is crucial for researchers and developers who need to customize their speech recognition systems.
- Comprehensive Toolset: The toolkit provides a wide range of tools for feature extraction (e.g., MFCCs, filter banks), acoustic modeling (including GMMs and DNNs), language modeling (n-gram and neural network-based), and decoding. This comprehensive set of tools makes Kaldi suitable for building state-of-the-art ASR models.
- Community Support and Documentation: Kaldi benefits from a strong community and extensive documentation, which are essential resources for troubleshooting and optimization. The active community ensures there are numerous recipes and scripts available for various tasks and datasets.
- Performance and Efficiency: Kaldi is known for its high accuracy and efficiency, making it suitable for both academic research and commercial applications. It supports real-time ASR systems, which is critical for applications requiring immediate transcription.
Who Would Benefit Most
- Researchers: Kaldi is particularly beneficial for researchers in the field of speech recognition. Its modern, flexible, and cleanly structured code, along with better support for WFST and math operations, make it an ideal choice for acoustic modeling research.
- Developers: Developers working on speech recognition projects can leverage Kaldi’s modular design to customize and extend the toolkit according to their specific needs. The extensive documentation and community support facilitate the development process.
- Organizations: Companies and organizations looking to integrate speech recognition into their products or services can benefit from Kaldi’s high accuracy and efficiency. It is widely used in speech services that are utilized by millions of people daily.
Overall Recommendation
Kaldi is highly recommended for anyone involved in speech recognition research or development. Its open-source nature, flexible design, and comprehensive toolset make it an invaluable resource. The strong community support and extensive documentation ensure that users can effectively utilize the toolkit to achieve high accuracy and efficiency in their speech recognition projects.
For those new to speech recognition, Kaldi’s ease of installation, data preparation scripts, and predefined recipes make it relatively straightforward to get started. For more advanced users, the ability to customize various aspects of the system, such as feature extraction, model training, and decoding, provides the flexibility needed to optimize performance.
In summary, Kaldi is a powerful and versatile toolkit that can significantly enhance the capabilities of anyone working in the field of speech recognition.