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Conversational Speech Naturalness Predictor
Anfeng Xu, Yashesh Gaur, Naoyuki Kanda +6
Evaluation of conversational naturalness is essential for developing human-like speech agents. However, existing speech naturalness predictors are often designed to assess utteranc…
T-Mimi: A Transformer-based Mimi Decoder for Real-Time On-Phone TTS
Haibin Wu, Bach Viet Do, Naveen Suda +10
Neural audio codecs provide promising acoustic features for speech synthesis, with representative streaming codecs like Mimi providing high-quality acoustic features for real-time…
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios
Aswin Shanmugam Subramanian, Amit Das, Naoyuki Kanda +3
We extend the frameworks of Serialized Output Training (SOT) to address practical needs of both streaming and offline automatic speech recognition (ASR) applications. Our approach…
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…
TS3-Codec: Transformer-Based Simple Streaming Single Codec
Haibin Wu, Naoyuki Kanda, Sefik Emre Eskimez +1
Neural audio codecs (NACs) have garnered significant attention as key technologies for audio compression as well as audio representation for speech language models. While mainstrea…
Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech
Haibin Wu, Xiaofei Wang, Sefik Emre Eskimez +8
People change their tones of voice, often accompanied by nonverbal vocalizations (NVs) such as laughter and cries, to convey rich emotions. However, most text-to-speech (TTS) syste…
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS
Xiaofei Wang, Sefik Emre Eskimez, Manthan Thakker +8
Recently, zero-shot text-to-speech (TTS) systems, capable of synthesizing any speaker's voice from a short audio prompt, have made rapid advancements. However, the quality of the g…
Total-Duration-Aware Duration Modeling for Text-to-Speech Systems
Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +9
Accurate control of the total duration of generated speech by adjusting the speech rate is crucial for various text-to-speech (TTS) applications. However, the impact of adjusting t…
E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
Sefik Emre Eskimez, Xiaofei Wang, Manthan Thakker +10
This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-autoregressive zero-shot text-to-speech system that offers human-level naturalness and state-of-the-a…
Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like
Naoyuki Kanda, Xiaofei Wang, Sefik Emre Eskimez +12
Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability…
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability
Jian Wu, Naoyuki Kanda, Takuya Yoshioka +3
Token-level serialized output training (t-SOT) was recently proposed to address the challenge of streaming multi-talker automatic speech recognition (ASR). T-SOT effectively handle…
DiariST: Streaming Speech Translation with Speaker Diarization
Mu Yang, Naoyuki Kanda, Xiaofei Wang +5
End-to-end speech translation (ST) for conversation recordings involves several under-explored challenges such as speaker diarization (SD) without accurate word time stamps and han…
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer
Xiaofei Wang, Manthan Thakker, Zhuo Chen +7
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models…
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers
Chenda Li, Yao Qian, Zhuo Chen +5
State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages. However, it remains…
Factual Consistency Oriented Speech Recognition
Naoyuki Kanda, Takuya Yoshioka, Yang Liu
This paper presents a novel optimization framework for automatic speech recognition (ASR) with the aim of reducing hallucinations produced by an ASR model. The proposed framework o…
Speech separation with large-scale self-supervised learning
Zhuo Chen, Naoyuki Kanda, Jian Wu +6
Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the ex…
Simulating realistic speech overlaps improves multi-talker ASR
Muqiao Yang, Naoyuki Kanda, Xiaofei Wang +5
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers. Due to the di…
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Zili Huang, Zhuo Chen, Naoyuki Kanda +6
Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts
Xiaofei Wang, Zhuo Chen, Yu Shi +3
Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model impr…
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational Speech Recognition
Naoyuki Kanda, Jian Wu, Xiaofei Wang +3
This paper presents a novel streaming automatic speech recognition (ASR) framework for multi-talker overlapping speech captured by a distant microphone array with an arbitrary geom…
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization
Dongmei Wang, Xiong Xiao, Naoyuki Kanda +2
This paper describes a speaker diarization model based on target speaker voice activity detection (TS-VAD) using transformers. To overcome the original TS-VAD model's drawback of b…
Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation
Xiaofei Wang, Dongmei Wang, Naoyuki Kanda +2
Existing multi-channel continuous speech separation (CSS) models are heavily dependent on supervised data - either simulated data which causes data mismatch between the training an…
Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings
Naoyuki Kanda, Jian Wu, Yu Wu +7
This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize ``who spoke what'' with low latency even when multiple people are…
Streaming Multi-Talker ASR with Token-Level Serialized Output Training
Naoyuki Kanda, Jian Wu, Yu Wu +7
This paper proposes a token-level serialized output training (t-SOT), a novel framework for streaming multi-talker automatic speech recognition (ASR). Unlike existing streaming mul…