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W. Ronny Huang

W. Ronny Huang

Ronny is a research scientist focused on building robust and generalizable algorithms for speech and language data. He has MS and PhD degrees from MIT electrical engineering where he demonstrated the first handheld laser-driven particle accelerator. More on his background here.
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Google Publications
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    Preview abstract 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. View details
    Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
    You-Chi Cheng
    IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022, {IEEE}, pp. 6097-6101
    Preview abstract Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierarchical word-and-character-based recurrent neural network model. We use the truecaser to normalize user-generated text in a Federated Learning framework for language modeling. A case-aware language model trained on this normalized text achieves the same perplexity as a model trained on text with gold capitalization. In a real user A/B experiment, we demonstrate that the improvement translates to reduced prediction error rates in a virtual keyboard application. Similarly, in an ASR language model fusion experiment, we show reduction in uppercase character error rate and word error rate. View details
    Preview abstract 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. View details
    Preview abstract End-to-end (E2E) models are often being accompanied by language models (LMs) via shallow fusion for boosting their overall quality as well as recognition of rare words. At the same time, several prior works show that LMs are susceptible to unintentionally memorizing rare or unique sequences in the training data. In this work, we design a framework for detecting memorization of random textual sequences (which we call canaries) in the LM training data when one has only black-box (query) access to LM-fused speech recognizer, as opposed to direct access to the LM. On a production-grade Conformer RNN-T E2E model fused with a Transformer LM, we show that detecting memorization of singly-occurring canaries from the LM training data of 300M examples is possible. Motivated to protect privacy, we also show that such memorization gets significantly reduced by per-example gradient-clipped LM training without compromising overall quality. View details
    Preview abstract We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by increasing the expressivity of the embedding table. In particular, we instantiate an (additional) embedding table which embeds the previous n-gram token sequence, rather than a single token. This allows the embedding table to be scaled up arbitrarily -- with a commensurate increase in performance -- without changing the token vocabulary. Since embeddings are sparsely retrieved from the table via a lookup; increasing the size of the table adds neither extra operations to each forward pass nor extra parameters that need to be stored on limited GPU/TPU memory. We explore scaling n-gram embedding tables up to nearly a billion parameters. When trained on a 3-billion sentence corpus, we find that LookupLM improves long tail log perplexity by 2.44 and long tail WER by 23.4% on a downstream speech recognition task over a standard RNN language model baseline, an improvement comparable to a scaling up the baseline by 6.2x the number of floating point operations. View details
    GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
    Chen Zhu
    Renkun Ni
    Kezhi Kong
    Tom Goldstein
    Conference on Neural Information Processing Systems (NeurIPS) (2021) (to appear)
    Preview abstract Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision. Unfortunately, novel architectures often result in challenging hyper-parameter choices and training instability if the network parameters are not properly initialized. A number of architecture-specific initialization schemes have been proposed, but these schemes are not always portable to new architectures. This paper presents GradInit, an automated and architecture agnostic method for initializing neural networks. GradInit is based on a simple heuristic; the norm of each network layer is adjusted so that a single step of SGD or Adam with prescribed hyperparameters results in the smallest possible loss value. This adjustment is done by introducing a scalar multiplier variable in front of each parameter block, and then optimizing these variables using a simple numerical scheme. GradInit accelerates the convergence and test performance of many convolutional architectures, both with or without skip connections, and even without normalization layers. It also improves the stability of the Post-LN Transformer for machine translation, enabling training it without learning rate warmup using either Adam or SGD under a wide range of learning rates and momentum coefficients. View details
    Preview abstract On-device end-to-end (E2E) models have shown improvementsover a conventional model on Search test sets in both quality, as measured by Word Error Rate (WER), and latency, measured by the time the result is finalized after the user stops speaking. However, the E2E model is trained on a small fraction of audio-text pairs compared to the 100 billion text utterances that a conventional language model (LM) is trained with. Thus E2E models perform poorly on rare words and phrases. In this paper, building upon the two-pass streaming Cascaded Encoder E2E model, we explore using a Hybrid Autoregressive Transducer (HAT) factorization to better integrate an on-device neural LM trained on text-only data. Furthermore, to further improve decoder latency we introduce a non-recurrent embedding decoder, in place of the typical LSTM decoder, into the Cascaded Encoder model. Overall, we present a streaming on-device model that incorporates an external neural LM and outperforms the conventional model in both search and rare-word quality, as well as latency, and is 318X smaller. View details
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