Quantifying audio aesthetics is challenging due to its subjective nature, influenced by human perception and cultural context. Traditional methods rely on human listeners, leading to inconsistencies and high resource demands. This paper addresses the growing need for automated systems capable of predicting audio aesthetics without human intervention. Such systems are crucial for applications like data filtering, pseudo-labeling, and evaluating generative models.In this paper, we propose new annotation guidelines that break down human listening perspectives into four axes and develop no-reference, peritem prediction models for more nuanced audio quality assessment. Our models are evaluated against human mean opinion scores (MOS) and existing methods, demonstrating comparable or superior performance. This research not only advances the field of audio aesthetics but also provides open-source models and datasets to facilitate future work and benchmarking.

Speech and language models are advancing towards universality. A single model can now handle translations across 200 languages and transcriptions for over 100 languages. Universal models simplify development, deployment, and importantly, transfer knowledge to less-resourced languages or modes. This paper introduces M2BART, a streamlined multilingual and multimodal framework for encoderdecoder models. It employs a self-supervised speech tokenizer, bridging speech and text, and is pre-trained with a unified objective for both unimodal and multimodal, unsupervised and supervised data. When tested on Spanish-to-English and English-to-Hokkien translations, M2BART consistently surpassed competitors. We also showcase an innovative translation model enabling zero-shot transfers even without labeled data.

Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong, Jingfei Du, Ann Lee, Vedanuj Goswami, Changhan Wang, Juan Pino, Benoît Sagot, Holger Schwenk. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Hirofumi Inaguma, Sravya Popuri, Ilia Kulikov, Peng-Jen Chen, Changhan Wang, Yu-An Chung, Yun Tang, Ann Lee, Shinji Watanabe, Juan Pino. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in expressive S2ST is limited, typically focusing on a single expressivity aspect at a time. Likewise, this research area lacks standard evaluation protocols and well-curated benchmark datasets. In this work, we propose a holistic cascade system for expressive S2ST, combining multiple prosody transfer techniques previously considered only in isolation. We curate a benchmark expressivity test set in the TV series domain and explored a second dataset in the audiobook domain. Finally, we present a human evaluation protocol to assess multiple expressive dimensions across speech pairs. Experimental results indicate that bi-lingual annotators can assess the quality of expressive preservation in S2ST systems, and the holistic modeling approach outperforms single-aspect systems. Audio samples can be accessed through our demo webpage: https://facebookresearch.github.io/speech_translation/cascade_expressive_s2st.

Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised models and textual pre-trained models, which have shown reasonable improvements to various SLU tasks. However, because of the mismatched modalities between speech signals and text tokens, previous methods usually need complex designs of the frameworks. This work proposes a simple yet efficient unsupervised paradigm that connects speech and textual pre-trained models, resulting in an unsupervised speech-to-semantic pre-trained model for various tasks in SLU. To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models. Our experiments show that unsupervised ASR itself can improve the representations from speech self-supervised models. More importantly, it is shown as an efficient connector between speech and textual pre-trained models, improving the performances of five different SLU tasks. Notably, on spoken question answering, we reach the state-of-the-art result over the challenging NMSQA benchmark.

It has been known that direct speech-to-speech translation (S2ST) models usually suffer from the data scarcity issue because of the limited existing parallel materials for both source and target speech. Therefore to train a direct S2ST system, previous works usually utilize text-to-speech (TTS) systems to generate samples in the target language by augmenting the data from speech-to-text translation (S2TT). However, there is a limited investigation into how the synthesized target speech would affect the S2ST models. In this work, we analyze the effect of changing synthesized target speech for direct S2ST models. We find that simply combining the target speech from different TTS systems can potentially improve the S2ST performances. Following that, we also propose a multi-task framework that jointly optimizes the S2ST system with multiple targets from different TTS systems. Extensive experiments demonstrate that our proposed framework achieves consistent improvements (2.8 BLEU) over the baselines on the Fisher Spanish-English dataset.

Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Ann Lee, Peng-Jen Chen, Changhan Wang, Jiatao Gu, Sravya Popuri, Xutai Ma, Adam Polyak, Yossi Adi, Qing He, Yun Tang, Juan Pino, Wei-Ning Hsu. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging. While computational needs have driven recent compiler, networking, and hardware advancements, utilization of those advancements by machine learning tools is occurring at a slower pace. This is in part due to the difficulties involved in prototyping new computational paradigms with existing frameworks. Large frameworks prioritize machine learning researchers and practitioners as end users and pay comparatively little attention to systems researchers who can push frameworks forward — we argue that both are equally important stakeholders. We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems by prioritizing open, modular, customizable internals and state-of-the-art, research-ready models and training setups across a variety of domains. Flashlight allows systems researchers to rapidly prototype and experiment with novel ideas in machine learning computation and has low overhead, competing with and often outperforming other popular machine learning frameworks. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.

Ann Lee, Hongyu Gong, Paul-Ambroise Duquenne, Holger Schwenk, Peng-Jen Chen, Changhan Wang, Sravya Popuri, Yossi Adi, Juan Pino, Jiatao Gu, Wei-Ning Hsu. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Ann Lee, Michael Auli, Marc’Aurelio Ranzato. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist large quantities of labeled and unlabeled data. In this paper, we explore more general setups where the domain of the unlabeled data for pre-training data differs from the domain of the labeled data for fine-tuning, which in turn may differ from the test data domain. Our experiments show that using target domain data during pre-training leads to large performance improvements across a variety of setups. On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%. This has obvious practical implications since it is much easier to obtain unlabeled target domain data than labeled data. Moreover, we find that pre-training on multiple domains improves generalization performance on domains not seen during training. Code and models will be made available at https://github.com/pytorch/fairseq.

For sequence transduction tasks like speech recognition, a strong structured prior model encodes rich information about the target space, implicitly ruling out invalid sequences by assigning them low probability. In this work, we propose local prior matching (LPM), a semi-supervised objective that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to an end-to-end model trained on unlabeled speech. We demonstrate that LPM is simple to implement and superior to existing knowledge distillation techniques under comparable settings. Starting from a baseline trained on 100 hours of labeled speech, with an additional 360 hours of unlabeled data, LPM recovers 54%/82% and 73%/91% of the word error rate on clean and noisy test sets with/without language model rescoring relative to a fully supervised model on the same data.

We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model. Key to our approach are a strong baseline acoustic and language model used to generate the pseudo-labels, filtering mechanisms tailored to common errors from sequence-to-sequence models, and a novel ensemble approach to increase pseudo-label diversity. Experiments on the LibriSpeech corpus show that with an ensemble of four models and label filtering, self-training yields a 33.9% relative improvement in WER compared with a baseline trained on 100 hours of labelled data in the noisy speech setting. In the clean speech setting, self-training recovers 59.3% of the gap between the baseline and an oracle model, which is at least 93.8% relatively higher than what previous approaches can achieve.

We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnitude more efficient than a strong RNN baseline. Key to our approach is a time-depth separable convolution block which dramatically reduces the number of parameters in the model while keeping the receptive field large. We also give a stable and efficient beam search inference procedure which allows us to effectively integrate a language model. Coupled with a convolutional language model, our time-depth separable convolution architecture improves by more than 22% relative WER over the best previously reported sequence-to-sequence results on the noisy LibriSpeech test set.

In this work, we introduce two improvements to our previously proposed mispronunciation detection framework. The framework focuses on each learner individually and consists of two main procedures: unsupervised error pattern discovery and pronunciation error decoding. First, we propose nbest filtering to disambiguate uncertain error candidate hypotheses obtained from acoustic similarity clustering. Second, we propose personalized template-based rescoring to refine the mispronunciation detection results. The second contribution of the paper is that we demonstrate the portability of the framework to a new target language. Experimental results on the iCALL corpus, a nonnative Mandarin corpus consisting of speakers of European origin, show that the new error pattern discovery process significantly reduces the size and increases the coverage of the error candidate set. Also, the rescoring technique effectively improves system performance on mispronunciation detection and diagnosis.

Conventional mispronunciation detection systems that have the capability of providing corrective feedback typically require a set of common error patterns that are known beforehand, obtained either by consulting with experts, or from a humanannotated nonnative corpus. In this paper, we propose a mispronunciation detection framework that does not rely on nonnative training data. We first discover an individual learner’s possible pronunciation error patterns by analyzing the acoustic similarities across their utterances. With the discovered error candidates, we iteratively compute forced alignments and decode learner-specific context-dependent error patterns in a greedy manner. We evaluate the framework on a Chinese University of Hong Kong (CUHK) corpus containing both Cantonese and Mandarin speakers reading English. Experimental results show that the proposed framework effectively detects mispronunciations and also has a good ability to prioritize feedback. Index Terms: Computer-Assisted Pronunciation Training (CAPT), Gaussian mixture model (GMM), Extended Recognition Network (ERN)

A Computer-Assisted Pronunciation Training (CAPT) system can provide greater benefit to language learners if it provides not only scoring but also corrective feedback. However, the process of deriving pronunciation error patterns usually requires linguistic knowledge, or large quantities of expensive, annotated, corpora from nonnative speakers. In this paper we explore the possibility of deriving context-dependent error patterns with limited human annotations. A two-stage labeling mechanism is proposed, which first selects a set of templates for human annotation, and then propagates the labels. To deal with the imbalanced number of correct and incorrect phone-level pronunciations in nonnative speech, pronunciation patterns on an individual learner-level are first summarized, and then corpuslevel clustering is done for template selection. The concept of contextual similarity based on a phonemic broad class definition is also proposed for label propagation. For evaluation, we view the task as an information retrieval task, and take advantage of metrics that consider both the importance and the ranking of an error type. Experimental results on a Chinese University of Hong Kong (CUHK) nonnative corpus show that the proposed framework can effectively discover prominent error patterns. Index Terms: Computer-Assisted Language Learning, unsupervised clustering, graph-based label propagation

In this paper, we explore the use of deep belief network (DBN) posteriorgrams as input to our previously proposed comparison-based system for detecting word-level mispronunciation. The system works by aligning a nonnative utterance with at least one native utterance and extracting features that describe the degree of mis-alignment from the aligned path and the distance matrix. We report system performance under different DBN training scenarios: pre-training and fine-tuning with either native data only or both native and nonnative data. Experimental results have shown that by substituting the system input from MFCC or Gaussian posteriorgrams obtained in a fully unsupervised manner to DBN posteriorgrams, the system performance can be improved by at least 10.4% relatively. Moreover, the system performance remains steady when only 30% of the annotations being used.

In this paper, we develop a sentence boundary detection system which incorporates a prosodic model, word and preterminal-level language models, and a global sentence-length model. An important aspect of this research was the investigation of crowdsourced punctuation annotations as a source of multiple references for evaluation purposes. In order to evaluate the system we propose a BLEU-like metric which compares a hypothesis to multiple references. Experiments on both transcription and ASR output show that the global sentence length model can improve the performance by 7.2 % on reference transcripts and 3.8 % on ASR output. Index Terms: sentence boundary detection, prosody, finite-state transducer, amazon mechanical turk

The task of mispronunciation detection for language learning is typically accomplished via automatic speech recognition (ASR). Unfortunately, less than 2% of the world's languages have an ASR capability, and the conventional process of creating an ASR system requires large quantities of expensive, annotated data. In this paper we report on our efforts to develop a comparison-based framework for detecting word-level mispronunciations in nonnative speech. Dynamic time warping (DTW) is carried out between a student's (non-native speaker) utterance and a teacher's (native speaker) utterance, and we focus on extracting word-level and phone-level features that describe the degree of mis-alignment in the warping path and the distance matrix. Experimental results on a Chinese University of Hong Kong (CUHK) nonnative corpus show that the proposed framework improves the relative performance on a mispronounced word detection task by nearly 50% compared to an approach that only considers DTW alignment scores.