4 citations · 18 across the 25 of their papers we have counts for
26 papers
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
Xin Wang, Héctor Delgado, Hemlata Tak +26
ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce…
SpoofCeleb: Speech Deepfake Detection and SASV In The Wild
Jee-weon Jung, Yihan Wu, Xin Wang +11
This paper introduces SpoofCeleb, a dataset designed for Speech Deepfake Detection (SDD) and Spoofing-robust Automatic Speaker Verification (SASV), utilizing source data from real-…
Text-To-Speech Synthesis In The Wild
Jee-weon Jung, Wangyou Zhang, Soumi Maiti +11
Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to util…
SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data
Yichen Lu, Jiaqi Song, Xuankai Chang +3
In this work, we present SynesLM, an unified model which can perform three multimodal language understanding tasks: audio-visual automatic speech recognition(AV-ASR) and visual-aid…
Towards Robust Speech Representation Learning for Thousands of Languages
William Chen, Wangyou Zhang, Yifan Peng +7
Self-supervised learning (SSL) has helped extend speech technologies to more languages by reducing the need for labeled data. However, models are still far from supporting the worl…
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages
Minsu Kim, Jee-weon Jung, Hyeongseop Rha +5
The capability to jointly process multi-modal information is becoming an essential task. However, the limited number of paired multi-modal data and the large computational requirem…
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition
Yihan Wu, Soumi Maiti, Yifan Peng +6
Recent advancements in language models have significantly enhanced performance in multiple speech-related tasks. Existing speech language models typically utilize task-dependent pr…
SpeechBERTScore: Reference-Aware Automatic Evaluation of Speech Generation Leveraging NLP Evaluation Metrics
Takaaki Saeki, Soumi Maiti, Shinnosuke Takamichi +2
While subjective assessments have been the gold standard for evaluating speech generation, there is a growing need for objective metrics that are highly correlated with human subje…
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Yifan Peng, Jinchuan Tian, Brian Yan +13
Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…
Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech
Dareen Alharthi, Roshan Sharma, Hira Dhamyal +3
Modern speech synthesis systems have improved significantly, with synthetic speech being indistinguishable from real speech. However, efficient and holistic evaluation of synthetic…
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning
William Chen, Jiatong Shi, Brian Yan +6
Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This furth…
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Xuankai Chang, Brian Yan, Kwanghee Choi +14
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features…
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens
Minsu Kim, Jeongsoo Choi, Soumi Maiti +3
In this paper, we propose methods to build a powerful and efficient Image-to-Speech captioning (Im2Sp) model. To this end, we start with importing the rich knowledge related to ima…
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks
Soumi Maiti, Yifan Peng, Shukjae Choi +3
We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates tex…
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute
William Chen, Xuankai Chang, Yifan Peng +3
Self-supervised learning (SSL) has led to great strides in speech processing. However, the resources needed to train these models has become prohibitively large as they continue to…
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
Brian Yan, Jiatong Shi, Yun Tang +13
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offlin…
Improving Massively Multilingual ASR With Auxiliary CTC Objectives
William Chen, Brian Yan, Jiatong Shi +3
Multilingual Automatic Speech Recognition (ASR) models have extended the usability of speech technologies to a wide variety of languages. With how many languages these models have…
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Massa Baali, Tomoki Hayashi, Hamdy Mubarak +4
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech. In contrast, low-resource languages, including Arabic, have very l…
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining
Takaaki Saeki, Soumi Maiti, Xinjian Li +3
While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text…
SpeechLMScore: Evaluating speech generation using speech language model
Soumi Maiti, Yifan Peng, Takaaki Saeki +1
While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming. Previous studies on automatic speech quality…
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
Soumi Maiti, Yushi Ueda, Shinji Watanabe +4
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting. Our proposed framework integrates speak…
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
Soumi Maiti, Hakan Erdogan, Kevin Wilson +3
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling spe…
Generating Multilingual Voices Using Speaker Space Translation Based on Bilingual Speaker Data
Soumi Maiti, Erik Marchi, Alistair Conkie
We present progress towards bilingual Text-to-Speech which is able to transform a monolingual voice to speak a second language while preserving speaker voice quality. We demonstrat…
Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement
Soumi Maiti, Michael I Mandel
Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better q…