We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data. The key to our approach is a self-supervised unit-based speech normalization technique, which finetunes a pre-trained speech encoder with paired audios from multiple speakers and a single reference speaker to reduce the variations due to accents, while preserving the lexical content. With only 10 minutes of paired data for speech normalization, we obtain on average 3.2 BLEU gain when training the S2ST model on the VoxPopuli S2ST dataset, compared to a baseline trained on un-normalized speech target. We also incorporate automatically mined S2ST data and show an additional 2.0 BLEU gain. To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs.

Textless Speech-to-Speech Translation on Real Data

textless-lib: a Library for Textless Spoken Language Processing

Generative spoken language modeling involves learning jointly the acoustic and linguistic characteristics of a language from raw audio only (without text or labels). We introduce metrics to automatically evaluate the generated output in terms of acoustic and linguistic quality in two associated end-to-end tasks, respectively: speech resynthesis (repeating the speech input using the system's own voice), and speech generation (producing novel speech outputs conditional on a spoken prompt, or unconditionally), and validate these metrics with human judgment. We test baseline systems consisting of a discrete speech encoder (returning discrete, low bitrate, pseudo-text units), a generative language model (trained on pseudo-text units), and a speech decoder (generating a waveform from pseudo-text). By comparing three state-of-the-art unsupervised speech encoders (Contrastive Predictive Coding (CPC), wav2vec 2.0, HuBERT), and varying the number of discrete units (50, 100, 200), we investigate how the generative performance depends on the quality of the learned units as measured by unsupervised metrics (zero-shot probe tasks). We will open source our evaluation stack and baseline models.

Generative Spoken Language Modeling from Raw Audio

We present FAIRSEQ S2, a FAIRSEQ extension for speech synthesis, including many models, tools and recipes for preprocessing and evaluation. This makes FAIRSEQ an all-in-one tool for speech/NLP.

fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit

In this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. Instead, we connect the image captioning module and the speech synthesis module with a set of discrete, sub-word speech units that are discovered with a self-supervised visual grounding task. We conduct experiments on the Flickr8k spoken caption dataset in addition to a novel corpus of spoken audio captions collected for the popular MSCOCO dataset, demonstrating that our generated captions also capture diverse visual semantics of the images they describe. We investigate several different intermediate speech representations, and empirically find that the representation must satisfy several important properties to serve as drop-in replacements for text.

Text-Free Image-to-Speech Synthesis Using Learned Segmental Units

9B: Speech & Phonology, Morphology

technical paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

6G: Industry/Demo Poster Session

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Virtual Poster Session I: Speech, Vision, Robotics, Multimodal Grounding

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Virtual Demo III

demo

Poster 3D: Speech and Multimodality

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As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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Registration is now open

IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Wei-Ning Hsu

5

5

SHORT BIO

Presentations

Textless Speech-to-Speech Translation on Real Data

textless-lib: a Library for Textless Spoken Language Processing

Generative Spoken Language Modeling from Raw Audio

fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit

Text-Free Image-to-Speech Synthesis Using Learned Segmental Units

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