Speech Technology

Announcing the VoiceMOS Challenge 2023! Challenge website: https://voicemos-challenge-2023.github.io Register to participate: https://forms.gle/kcLc69Wa4Q97rSNq7 This edition of the challenge will focus on real-world and challenging zero-shot out-of-domain mean opinion score prediction! https://twitter.com/yamagishilab/status/1643788523886235648

CALLS: Japanese Empathetic Dialogue Speech Corpus of Complaint Handling and Attentive Listening in Customer Center Yuki Saito, Eiji Iimori, Shinnosuke Takamichi, Kentaro Tachibana, Hiroshi Saruwatari We present CALLS, a Japanese speech corpus that considers phone calls in a customer center as a new domain of empathetic spoken dialogue. The existing STUDIES corpus covers only empathetic dialogue between a teacher and student in a school. To extend the application range of empathetic dialogue speech synthesis (EDSS), we designed our corpus to include the same female speaker as the STUDIES teacher, acting as an operator in simulated phone calls. We describe a corpus construction methodology and analyze the recorded speech. We also conduct EDSS experiments using the CALLS and STUDIES corpora to investigate the effect of domain differences. The results show that mixing the two corpora during training causes biased improvements in the quality of synthetic speech due to the different degrees of expressiveness. Our project page of the corpus is this http URL. https://arxiv.org/abs/2305.13713 https://sython.org/Corpus/STUDIES-2/

In case you want to play with laughter https://twitter.com/forthshinji/status/1660990946606219266

MMS: Massively Multilingual Speech. - Can do speech2text and text speech in 1100 languages. - Can recognize 4000 spoken languages. - Code and models available under the CC-BY-NC 4.0 license. - half the word error rate of Whisper. Code+Models: https://github.com/facebookresearch/fairseq/tree/main/examples/mms Paper: https://scontent-lga3-2.xx.fbcdn.net/v/t39.8562-6/348836647_265923086001014_6878005808275791319_n.pdf Blog: https://ai.facebook.com/blog/multilingual-model-speech-recognition/

https://arxiv.org/abs/2305.11834 Pengi: An Audio Language Model for Audio Tasks Soham Deshmukh, Benjamin Elizalde, Rita Singh, Huaming Wang In the domain of audio processing, Transfer Learning has facilitated the rise of Self-Supervised Learning and Zero-Shot Learning techniques. These approaches have led to the development of versatile models capable of tackling a wide array of tasks, while delivering state-of-the-art performance. However, current models inherently lack the capacity to produce the requisite language for open-ended tasks, such as Audio Captioning or Audio Question & Answering. We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks. It takes as input, an audio recording, and text, and generates free-form text as output. The input audio is represented as a sequence of continuous embeddings by an audio encoder. A text encoder does the same for the corresponding text input. Both sequences are combined as a prefix to prompt a pre-trained frozen language model. The unified architecture of Pengi enables open-ended tasks and close-ended tasks without any additional fine-tuning or task-specific extensions. When evaluated on 22 downstream tasks, our approach yields state-of-the-art performance in several of them. Our results show that connecting language models with audio models is a major step towards general-purpose audio understanding

More details on Soundstorm https://twitter.com/danlyth/status/1660608450852691968 SoundStorm does a nice job of alleviating a key shortcoming of AudioLM. By replacing the somewhat cumbersome and slow dual Transformers required for the acoustic token generation, they use bi-directional parallel decoding, leading to a speed-up of two orders of magnitude.

Interactive demo available https://github.com/YuanGongND/ltu

https://twitter.com/csteinmetz1/status/1659458441197355008 I was complaining that LLMs don't have ears... This paper is a solid attempt to try to make that happen. abs: https://arxiv.org/abs/2305.10790 Work from Yuan Gong et al. at MIT

Whisper is essentially an audio-conditioned LLM. Can we prompt it to do unseen tasks? Introducing PromptingWhisper! We use simple prompts to adapt Whisper to unseen tasks zero-shot without any finetuning. 📄 Paper: http://arxiv.org/abs/2305.11095 💻 Code: https://github.com/jasonppy/PromptingWhisper

3 nice Persian TTS datasets https://www.kaggle.com/magnoliasis/datasets

Final VoxCeleb Challenge https://mm.kaist.ac.kr/datasets/voxceleb/voxsrc/competition2023.html Timeline May 20th Development set for verification tracks released. May 31rd Development set for diarisation tracks released. June 1st Test set released and evaluation server open. Early August Deadline for submission of results; invitation to workshop speakers. August 20th Challenge workshop

Recent advances in the AudioLM family: 100x higher speed, better consistency, no quality hit - a new paper from and the AudioLM team. Give it a listen: https://google-research.github.io/seanet/soundstorm/examples/ Arxiv: https://arxiv.org/abs/2305.09636

Multilingual TTS from ElevenLabs https://twitter.com/radamar/status/1658540025611685888

Some nice things from industry, autoscaling with Triton and Kubernetes https://www.speechmatics.com/company/articles-and-news/autoscaling-with-gpu-transcription-models

The first Arabic TTS Challenge - QASR TTS 1.0 is on!! Register and build your own Arabic Anchor Voice and contribute to enriching #ArabicAI #ASRU2023Challege More details: https://arabicspeech.org/qasr-challenge/ @QatarComputing

Some people implement streaming speaker diarization manually https://github.com/pyannote/pyannote-audio/commit/4a6ea9c825b9447a7d03cb9bd94f5f81d661ca16 others just ask ChatGPT to write it https://github.com/huseinzol05/malaya-speech/commit/564f50c0d91528126fe3b410f387d1b4ff33d364 ChatGPT version is not that bad

Universal Source Separation with Weakly Labelled Data abs: https://arxiv.org/abs/2305.07447 paper page: https://huggingface.co/papers/2305.07447 github: https://github.com/bytedance/uss