Transfer learning of language-independent end-to-end ASR with language model fusion

This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-independent ASR system in a unified sequence-to-sequence (S2S) architecture with a shared vocabulary among all languages. During adaptation, we perform LM fusion transfer, where an external LM is integrated into the decoder network of the attention-based S2S model in the whole adaptation stage, to effectively incorporate linguistic context of the target language. We also investigate various seed models for transfer learning. Experimental evaluations using the IARPA BABEL data set show that LM fusion transfer improves performances on all target five languages compared with simple transfer learning when the external text data is available. Our final system drastically reduces the performance gap from the hybrid systems.Comment: Accepted at ICASSP201

Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. Researchers and industry prefer to use end-to-end ASR systems for on-device speech recognition tasks. This is because end-to-end systems can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Another challenging task associated with speech assistants is personalization, which mainly lies in handling out-of-vocabulary (OOV) words. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. To address the aforementioned problems, we propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. It non-deterministically tokenizes utterances to extend the token's contexts and to regularize their distribution for the model's recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative WER and 25% relative F-score) at no additional computational cost. Owing to the use of BPE-dropout, our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is close to the best published multilingual system.Comment: 16 pages, 7 figure

Neural approaches to spoken content embedding

Comparing spoken segments is a central operation to speech processing. Traditional approaches in this area have favored frame-level dynamic programming algorithms, such as dynamic time warping, because they require no supervision, but they are limited in performance and efficiency. As an alternative, acoustic word embeddings -- fixed-dimensional vector representations of variable-length spoken word segments -- have begun to be considered for such tasks as well. However, the current space of such discriminative embedding models, training approaches, and their application to real-world downstream tasks is limited. We start by considering ``single-view" training losses where the goal is to learn an acoustic word embedding model that separates same-word and different-word spoken segment pairs. Then, we consider ``multi-view" contrastive losses. In this setting, acoustic word embeddings are learned jointly with embeddings of character sequences to generate acoustically grounded embeddings of written words, or acoustically grounded word embeddings. In this thesis, we contribute new discriminative acoustic word embedding (AWE) and acoustically grounded word embedding (AGWE) approaches based on recurrent neural networks (RNNs). We improve model training in terms of both efficiency and performance. We take these developments beyond English to several low-resource languages and show that multilingual training improves performance when labeled data is limited. We apply our embedding models, both monolingual and multilingual, to the downstream tasks of query-by-example speech search and automatic speech recognition. Finally, we show how our embedding approaches compare with and complement more recent self-supervised speech models.Comment: PhD thesi

Multilingual Deep Bottle Neck Features: A Study on Language Selection and Training Techniques

Previous work has shown that training the neural networks for bottle neck feature extraction in a multilingual way can lead to improvements in word error rate and average term weighted value in a telephone key word search task. In this work we conduct a systematic study on a) which multilingual training strategy to employ, b) the effect of language selection and amount of multilingual training data used and c) how to find a suitable combination for languages. We conducted our experiment on the key word search task and the languages of the IARPA BABEL program. In a first step, we assessed the performance of a single language out of all available languages in combination with the target language. Based on these results, we then combined a multitude of languages. We also examined the influence of the amount of training data per language, as well as different techniques for combining the languages during network training. Our experiments show that data from arbitrary additional languages does not necessarily increase the performance of a system. But when combining a suitable set of languages, a significant gain in performance can be achieved

Multilingual representations for low resource speech recognition and keyword search

© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation

Investigating techniques for low resource conversational speech recognition

International audienceIn this paper we investigate various techniques in order to build effective speech to text (STT) and keyword search (KWS) systems for low resource conversational speech. Sub-word decoding and graphemic mappings were assessed in order to detect out-of-vocabulary keywords. To deal with the limited amount of transcribed data, semi-supervised training and data selection methods were investigated. Robust acoustic features produced via data augmentation were evaluated for acoustic modeling. For language modeling, automatically retrieved conversational-like Webdata was used, as well as neural network based models. We report STT improvements with all the techniques, but interestingly only some improve KWS performance. Results are reported for the Swahili language in the context of the 2015 OpenKWS Evaluation

Log-linear system combination using structured support vector machines

Building high accuracy speech recognition systems with limited language resources is a highly challenging task. Although the use of multi-language data for acoustic models yields improvements, performance is often unsatisfactory with highly limited acoustic training data. In these situations, it is possible to consider using multiple well trained acoustic models and combine the system outputs together. Unfortunately, the computational cost associated with these approaches is high as multiple decoding runs are required. To address this problem, this paper examines schemes based on log-linear score combination. This has a number of advantages over standard combination schemes. Even with limited acoustic training data, it is possible to train, for example, phone-specific combination weights, allowing detailed relationships between the available well trained models to be obtained. To ensure robust parameter estimation, this paper casts log-linear score combination into a structured support vector machine (SSVM) learning task. This yields a method to train model parameters with good generalisation properties. Here the SSVM feature space is a set of scores from well-trained individual systems. The SSVM approach is compared to lattice rescoring and confusion network combination using language packs released within the IARPA Babel program

SeamlessM4T-Massively Multilingual & Multimodal Machine Translation

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communicatio