Tara N. Sainath
Tara Sainath received her S.B., M.Eng and PhD in Electrical Engineering and Computer Science (EECS) from MIT. After her PhD, she spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. She has served as a Program Chair for ICLR in 2017 and 2018. Also, she has co-organized numerous special sessions and workshops, including Interspeech 2010, ICML 2013, Interspeech 2016, ICML 2017, Interspeech 2019, NeurIPS 2020. In addition, she has served as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) as well as the Associate Editor for IEEE/ACM Transactions on Audio, Speech, and Language Processing. She is an IEEE and ISCA Fellow. In addition, she is the recipient of the 2021 IEEE SPS Industrial Innovation Award as well as the 2022 IEEE SPS Signal Processing Magazine Best Paper Award. She is currently a Principal Research Scientist at Google, working on applications of deep neural networks for automatic speech recognition.
Authored Publications
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Speech data from different domains has distinct acoustic and linguistic characteristics. It is common to train a single multidomain model such as a Conformer transducer for speech recognition on a mixture of data from all domains. However, changing data in one domain or adding a new domain would require the multidomain model to be retrained. To this end, we propose a framework called modular domain adaptation (MDA) that enables a single model to process multidomain data while keeping all parameters domain-specific, i.e., each parameter is only trained by data from one domain. On a streaming Conformer transducer trained only on video caption data, experimental results show that an MDA-based model can reach similar performance as the multidomain model on other domains such as voice search and dictation by adding per-domain adapters and per-domain feed-forward networks in the Conformer encoder.
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Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate. This study examines the use of text injection for auxiliary tasks, which are the non-ASR tasks often performed by an E2E model. In this work, we use joint end-to-end and internal language model training (JEIT) as our text injection algorithm to train an ASR model which performs two auxiliary tasks. The first is capitalization, which is a de-normalization task. The second is turn-taking prediction, which attempts to identify whether a user has completed their conversation turn in a digital assistant interaction. We show results demonstrating that our text injection method boosts capitalization performance for long-tail data, and improves turn-taking detection recall.
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Transducer-Based Streaming Deliberation For A Cascaded Encoder Model
Kevin Hu
Ruoming Pang
ICASSP 2022(2022) (to appear)
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Previous research on deliberation networks has achieved excellent recognition quality. The attention decoder based deliberation models often works as a rescorer to improve first-pass recognition results, and often requires the full first-pass hypothesis for second-pass deliberation. In this work, we propose a streaming transducer-based deliberation model. The joint network of a transducer decoder often consists of inputs from the encoder and the prediction network. We propose to use attention to the first-pass text hypotheses as the third input to the joint network. The proposed transducer based deliberation model naturally streams, making it more desirable for on-device applications. We also show that the model improves rare word recognition, with relative WER reductions ranging from 3.6% to 10.4% for a variety of test sets. Our model does not use any additional text data for training.
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Improving Deliberation by Text-Only and Semi-Supervised Training
Kevin Hu
Weiran Wang
Yanzhang (Ryan) He
Interspeech 2022(2022) (to appear)
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Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text or speech data. In this work, we propose text-only and semi-supervised training for attention-decoder based deliberation. By incorporating text-only data in training a bidirectional encoder representation from transformer (BERT) for the deliberation text encoder, joint acoustic and text decoder (JATD) training, and semi-supervised training based on a conventional model as a teacher, we achieved up to 11.7% WER reduction compared to the baseline deliberation. Compared to a state-of-the-art language model (LM) rescoring method, the deliberation model reduces the WER by 8% relative for Google Voice Search with reasonable endpointing latencies. We show that the deliberation has achieved a positive human side-by-side evaluation compared to LM rescoring.
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Multilingual end-to-end automatic speech recognition models are attractive due to its simplicity in training and deployment. Recent work on large-scale training of such models has shown promising results compared to monolingual models. However, the work often focuses on the structure of multilingual models themselves in a single-pass decoding setup. In this work, we investigate second-pass deliberation for multilingual speech recognition. Our proposed deliberation is multilingual, i.e., the text encoder encodes hypothesis text from multiple languages, and the deliberation decoder attends to encoded text and audio from multiple languages without explicitly using language information. We investigate scaling different components of the multilingual deliberation model, such as the text encoder and deliberation decoder, and also compare scaling the second-pass deliberation decoder and the first-pass cascaded encoder. We show that deliberation improves the average WER on 9 languages by 4% relative compared to the single-pass model in a truly multilingual setup. By increasing the size of the deliberation model up to 1B parameters, the average WER improvement increases to 9%, with up to 14% for certain languages.
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Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems
Bo Li
Chao Zhang
Yanzhang he
IEEE Spoken Language Technology Workshop(2022)
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Automatic speech recognition (ASR) systems typically rely on an external endpointer (EP) model to identify speech boundaries. This EP model strongly affects latency, but is subject to computational constraints, which limits prediction accuracy. We propose a method to jointly train the ASR and EP tasks in a single end-to-end (E2E) multitask model, improving EP quality by optionally leveraging information from the ASR audio encoder. We introduce a "switch" connection, which trains the EP to consume either the audio frames directly or low-level latent representations from the ASR model. This allows flexibility during inference to produce a low-cost prediction or a higher quality prediction if ASR computation is ongoing. We present results on a voice search test set showing that, compared to separate single-task models, this approach reduces median endpoint latency by 130ms (33.3% reduction), and 90th percentile latency by 160ms (22.2% reduction), without regressing word-error rate. For continuous recognition, WER improves by 10.6% (relative).
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E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR
David Rybach
Zhiyun Lu
Interspeech 2022(2022) (to appear)
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Improving the performance of end-to-end ASR models on long utterances of minutes to hours is an ongoing problem in speech recognition.
A common solution is to segment the audio in advance using a separate voice activity detector (VAD) that decides segment boundaries based purely on acoustic speech/non-speech information. VAD segmenters, however, may be sub-optimal for real-world speech where, e.g., a complete sentence that should be taken as a whole may contain hesitations in the middle ("set a alarm for... 5 o'clock").
Here, we propose replacing the VAD with an end-to-end ASR model capable of predicting segment boundaries, allowing the segmentation to be conditioned not only on deeper acoustic features but also on linguistic features from the decoded text, while requiring negligible extra compute.
In experiments on real world long-form audio (YouTube) of up to 30 minutes long, we demonstrate WER gains of 5\% relative to the VAD baseline on a state-of-the-art Conformer RNN-T setup.
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SENTENCE-SELECT: LARGE-SCALE LANGUAGE MODEL DATA SELECTION FOR RARE-WORD SPEECH RECOGNITION
Ruoming Pang
Submitted to interspeech 2022(2022) (to appear)
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Language model fusion can help smart assistants recognize tail words which are rare in acoustic data but abundant in text-only corpora.
However, large-scale text corpora sourced from typed chat or search logs are often (1) prohibitively expensive to train on, (2) beset with content that is mismatched to the voice domain, and (3) heavy-headed rather than heavy-tailed (e.g., too many common search queries such as ``weather''), hindering downstream performance gains.
We show that three simple strategies for selecting language modeling data can dramatically improve rare-word recognition without harming overall performance.
First, to address the heavy-headedness, we downsample the data according to a soft log function, which tunably reduces high frequency (head) sentences.
Second, to encourage rare-word accuracy, we explicitly filter for sentences with words which are rare in the acoustic data.
Finally, we tackle domain-mismatch by apply perplexity-based contrastive selection to filter for examples which are matched to the target domain.
We downselect a large corpus of web search queries by a factor of over 50x to train an LM, achieving better perplexities on the target acoustic domain than without downselection.
When used with shallow fusion on a production-grade speech engine, it achieves a WER reduction of up to 24\% on rare-word sentences (without changing the overall WER) relative to a baseline LM trained on an unfiltered corpus.
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Transformer Based Deliberation for Two-Pass Speech Recognition
Kevin Hu
Ruoming Pang
IEEE Spoken Language Technology Workshop(2021)
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Interactive speech recognition systems must generate words quickly while also producing accurate results. Two-pass models excel at these requirements by employing a first-pass decoder that quickly emits words, and a second-pass decoder that requires more context but is more accurate. Previous work has established that deliberation networks can be effective second-pass models. These models accept two kinds of inputs at once: encoded audio frames and the hypothesis text from the first-pass model. In this work, we explore using transformer layers instead of long-short term memory (LSTM) layers for deliberation rescoring. In transformer layers, we generalize the ``encoder-decoder" attention to attend to both encoded audio and first-pass text hypotheses. The output context vectors are then combined by a merger layer. Compared to LSTM-based deliberation, our best transformer deliberation achieves 7% relative word error rate (WER) improvements along with a 38% reduction in computation. We also compare against a non-deliberation transformer rescoring, and find a 9% relative improvement.
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We propose a multitask training method for attention-basedend-to-end speech recognition models. We regularize the de-coder in a listen, attend, and spell model by multitask trainingon both audio-text and text-only data. Trained on the 100-hoursubset of LibriSpeech, the proposed method leads to an 11%relative performance improvement over the baseline and is com-parable to language model shallow fusion, without requiring anadditional neural network during decoding. We observe a simi-lar trend on the whole 960-hour LibriSpeech training set. Anal-yses of sample output sentences demonstrate that the proposedmethod can incorporate language level information, suggestingits effectiveness in real-world applications
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