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Articles by Athanasios Mouchtaris
Phonetically induced subwords for end-to-end speech recognition
End-to-end automatic speech recognition systems map a sequence of acoustic features to text. In modern systems, text is encoded to grapheme subwords which are generated by methods designed for text processing tasks and therefore don’t model or take advantage of the statistics of the acoustic features. Here, we present a novel method for generating grapheme subwords that are derived from phoneme sequences, therefore capturing phonetical statistics.
Context-aware Transformer transducer for speech recognition
End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty recognizing uncommon words, that appear infrequently in the training data. One promising method, to improve the recognition accuracy on such rare words, is to latch onto personalized/contextual information at inference. In this work, we present a novel context-aware transformer transducer (CATT) network that improves the state-of-the-art transformer-based ASR system by taking advantage of such contextual signals.
End-to-end spoken language understanding for generalized voice assistants
End-to-end (E2E) spoken language understanding (SLU) systems predict utterance semantics directly from speech using a single model. Previous work in this area has focused on targeted tasks in fixed domains, where the output semantic structure is assumed a priori and the input speech is of limited complexity. In this work we present our approach to developing an E2E model for generalized SLU in commercial voice assistants (VAs).
Sparsification via compressed sensing for automatic speech recognition
In order to achieve high accuracy for machine learning (ML) applications, it is essential to employ models with a large number of parameters. Certain applications, such as Automatic Speech Recognition (ASR), however, require real-time interactions with users, hence compelling the model to have as low latency as possible. Deploying large scale ML applications thus necessitates model quantization and compression, especially when running ML models on resource constrained devices.
Knowledge distillation via module replacing for automatic speech recognition with recurrent neural network transducer
Automatic Speech Recognition (ASR) is increasingly used by edge applications such as intelligent virtual assistants. However, state-of-the-art ASR models such as Recurrent Neural Network - Transducer (RNN-T) are computationally intensive on resource-constrained edge devices. Knowledge Distillation (KD) is a promising approach to compress large models by using a large model (”teacher”) to train a small model (”student”).
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