Ankur Bapna
I am a Staff Software Engineer on the Brain team. My current research interests include multimodal representation learning for speech and text, massively multilingual modeling and applications of these approaches to translation, ASR, TTS and tasks involving end-to-end speech understanding and generation.
Authored Publications
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Multimodal Modeling for Spoken Language Identification
Shikhar Bharadwaj
Sriram (Sri) Ganapathy
Sid Dalmia
Wei Han
Yu Zhang
Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024) (2024)
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Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language identification task. Prior techniques have been constrained to a single modality; however in the case of video data there is a wealth of other metadata that may be beneficial for this task. In this work, we propose MuSeLI, a Multimodal Spoken Language Identification method, which delves into the use of various metadata sources to enhance language identification. Our study reveals that metadata such as video title, description and geographic location provide substantial information to identify the spoken language of the multimedia recording. We conduct experiments using two diverse public datasets of YouTube videos, and obtain state-of-the-art results on the language identification task. We additionally conduct an ablation study that describes the distinct contribution of each modality for language recognition.
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We present Mu2SLAM, a multilingual sequence-to-sequence model pre-trained jointly on un-labeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition(ASR), Automatic Speech Translation (AST)and Machine Translation (MT), in over 100 languages. By leveraging a quantized representation of speech as a target, Mu2SLAM trains ona sequence-to-sequence masked denoising objective similar to T5 on both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoSTAST, Mu2SLAM establishes a new state-of-the-art for models trained on public datasets, improv-ing on xx-en translation over the previous best by 1.9 Bleu points and on en-xx translation by 0.9 Bleu points. On Voxpopuli ASR, our model matches the performance of a mSLAM model finetuned with a RNN-T decoder, despite using a relatively weaker sequence-to-sequence architecture. On text understanding tasks, our model improves by more than 6% over mSLAM on XNLI, getting closer to the performance of mT5 models of comparable capacity on XNLI and TydiQA, paving the way towards a single model for all speech and text understanding tasks.
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Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech Representation and Linguistic Features
Yifan Ding
Kohei Yatabe
Nobuyuki Morioka
Yu Zhang
Wei Han
WASPAA 2023 (2023) (to appear)
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Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the web to studio-quality. To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) linguistic features extracted from transcripts and PnG-BERT for conditioning features. Experiments show that the proposed model (i) is robust against various audio degradation, (ii) can restore samples in the LJspeech dataset and improves the quality of text-to-speech (TTS) outputs without changing the model and hyper-parameters, and (iii) enable us to train a high-quality TTS model from restored speech samples collected from the web.
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LibriTTS-R: Restoration of a Large-Scale Multi-Speaker TTS Corpus
Yifan Ding
Kohei Yatabe
Nobuyuki Morioka
Yu Zhang
Wei Han
Interspeech 2023 (2023)
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This paper introduces a new speech dataset called ``LibriTTS-R'' designed for text-to-speech (TTS) use. It is derived by applying speech restoration to the LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling rate from 2,456 speakers and the corresponding texts. The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved. Experimental results show that the LibriTTS-R ground-truth samples showed significantly improved sound quality compared to those in LibriTTS. In addition, neural end-to-end TTS trained with LibriTTS-R achieved speech naturalness on par with that of the ground-truth samples. The corpus is freely available for download from [URL-HERE]
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Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-to-Speech
Takaaki Saeki
Zhehuai Chen
Nobuyuki Morioka
Yu Zhang
ICASSP (2023)
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This paper proposes Virtuoso, a massive multilingual speech–text joint learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typically supports tens of languages, which are a small fraction of thousands of languages in the world. One difficulty to scale multilingual TTS to hundreds of languages is collecting high-quality speech–text paired data in low-resource languages. This study extends Maestro, which is a speech–text semi-supervised joint pretraining framework for automatic speech recognition (ASR), to speech generation tasks. To train a TTS model from various types of speech and text data, different training schemes are designed to handle supervised (paired TTS and ASR data) and unsupervised (untranscribed speech and unspoken text) datasets. Experimental evaluation shows that 1) multilingual TTS models trained on Virtuoso can achieve significantly better naturalness and intelligibility than baseline TTS models in seen languages, and 2) these models can synthesize reasonably good speech for unseen languages where no paired TTS data is available.
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Label Aware Speech Representation Learning For Language Identification
Shikhar Bharadwaj
Sriram Ganapathy
Wei Han
Proceedings of Interspeech 2023, pp. 5351-5355
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The speech representation learning approaches, for nonsemantic tasks like language recognition, have either explored supervised embedding extraction methods using a classifier model or the self-supervised representation learning approach using raw data. In this paper, we propose a novel framework of combining the self-supervised representation learning with the language label information for the pre-training task. This framework, termed as label aware speech representation learning (LASR), uses a triplet based objective function to incorporate the language labels along with the self-supervised loss function. The speech representations are further fine-tuned for the identification task. The language recognition experiments are performed on two public datasets - FLEURS and Dhwani. In these experiments, we illustrate that the proposed LASR framework improves over the state-of-art systems in terms of recognition performance. We also report an analysis of the robustness of the LASR approach to noisy/missing labels as well as the application of the LASR model for downstream multi-lingual speech recognition tasks.
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MAESTRO: Matched Speech Text Representations through Modality Matching
Pedro Jose Moreno Mengibar
Yu Zhang
Zhehuai Chen
interspeech 2022 (2022) (to appear)
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We present Maestro, a self-supervised training method to
unify representations learnt from speech and text modalities.
Self-supervised learning from speech signals aims to learn the
latent structure inherent in the signal, while self-supervised
learning from text attempts to capture lexical information.
Learning aligned representations from unpaired speech and
text sequences is a challenging task. Previous work either
implicitly enforced the representations learnt from these two
modalities to be aligned in the latent space through multi-
tasking and parameter sharing or explicitly through conversion
of modalities via speech synthesis. While the former suffers
from interference between the two modalities, the latter
introduces additional complexity. In this paper, we propose
Maestro, a novel algorithm to learn unified representations from
both these modalities simultaneously that can transfer to diverse
downstream tasks such as Automated Speech Recognition
(ASR) and Speech Translation (ST). Maestro learns unified
representations through sequence alignment, duration predic-
tion and matching embeddings in the learned space through
an aligned masked-language model loss. We establish a new
state-of-the-art (SOTA) on VoxPopuli multilingual ASR with
a 8% relative reduction in Word Error Rate (WER), multi-
domain SpeechStew ASR (3.7% relative) and 21 languages to
English multilingual ST on CoVoST 2 with an improvement of
2.8 BLEU averaged over 21 languages.
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Multilingual neural machine translation (NMT) typically learns to maximize the likelihood of training examples from a combination set of multiple language pairs. However, this mechanical combination only relies on the basic sharing to learn the inductive bias, which undermines the generalization and transferability of multilingual NMT models. In this paper, we introduce a multilingual crossover encoder-decoder (mXEnDec) to fuse language pairs at instance level to exploit cross-lingual signals. For better fusions on multilingual data, we propose several techniques to deal with the language interpolation, dissimilar language fusion and heavy data imbalance. Experimental results on a large-scale WMT multilingual data set show that our approach significantly improves model performance on general multilingual test sets and the model transferability on zero-shot test sets (up to $+5.53$ BLEU).
Results on noisy inputs demonstrates the capability of our approach to improve model robustness against the code-switching noise. We also conduct qualitative and quantitative representation comparisons to analyze the advantages of our approach at the representation level.
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Building Machine Translation Systems for the Next Thousand Languages
Julia Kreutzer
Mengmeng Niu
Pallavi Nikhil Baljekar
Xavier Garcia
Maxim Krikun
Pidong Wang
Apu Shah
Macduff Richard Hughes
Google Research (2022)
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
Alexis Conneau
Simran Khanuja
Yu Zhang
Siddharth Dalmia
Clara Rivera
IEEE Spoken Language Technology Workshop (SLT) (2022)
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We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.
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