158 citations · 895 across the 103 of their papers we have counts for
19 papers · 1 filter
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Yiming Chen, Yan Zhang, Bin Wang +2
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the performanc…
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text
Xianghu Yue, Junyi Ao, Xiaoxue Gao +1
Self-supervised pre-training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are a…
FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis
Yifan Hu, Rui Liu, Guanglai Gao +1
Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. The correlation between the current…
Speech Synthesis with Mixed Emotions
Kun Zhou, Berrak Sisman, Rajib Rana +2
Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a spec…
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database
Jinming Zhao, Tenggan Zhang, Jingwen Hu +4
The emotional state of a speaker can be influenced by many different factors in dialogues, such as dialogue scene, dialogue topic, and interlocutor stimulus. The currently availabl…
Revisiting Self-Training for Few-Shot Learning of Language Model
Yiming Chen, Yan Zhang, Chen Zhang +3
As unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In…
Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification
Bidisha Sharma, Maulik Madhavi, Xuehao Zhou +1
End-to-end speech-to-intent classification has shown its advantage in harvesting information from both text and speech. In this paper, we study a technique to develop such an end-t…
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification
Yidi Jiang, Bidisha Sharma, Maulik Madhavi +1
End-to-end intent classification using speech has numerous advantages compared to the conventional pipeline approach using automatic speech recognition (ASR), followed by natural l…
DynaEval: Unifying Turn and Dialogue Level Evaluation
Chen Zhang, Yiming Chen, Luis Fernando D'Haro +4
A dialogue is essentially a multi-turn interaction among interlocutors. Effective evaluation metrics should reflect the dynamics of such interaction. Existing automatic metrics are…
Emotional Voice Conversion: Theory, Databases and ESD
Kun Zhou, Berrak Sisman, Rui Liu +1
In this paper, we first provide a review of the state-of-the-art emotional voice conversion research, and the existing emotional speech databases. We then motivate the development…
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability
Rui Liu, Berrak Sisman, Haizhou Li
Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. However, the generated voice is often not perceptually identifiable by its intended emotion catego…
Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training
Kun Zhou, Berrak Sisman, Haizhou Li
Emotional voice conversion (EVC) aims to change the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we propose a novel…
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification
Bidisha Sharma, Maulik Madhavi, Haizhou Li
Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module fol…
Independent language modeling architecture for end-to-end ASR
Van Tung Pham, Haihua Xu, Yerbolat Khassanov +5
The attention-based end-to-end (E2E) automatic speech recognition (ASR) architecture allows for joint optimization of acoustic and language models within a single network. However,…
Teacher-Student Training for Robust Tacotron-based TTS
Rui Liu, Berrak Sisman, Jingdong Li +3
While neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways, the exposure bias problem in the autoregressive models remains an issue t…
End-to-End Code-Switching ASR for Low-Resourced Language Pairs
Xianghu Yue, Grandee Lee, Emre Yılmaz +2
Despite the significant progress in end-to-end (E2E) automatic speech recognition (ASR), E2E ASR for low resourced code-switching (CS) speech has not been well studied. In this wor…
Large-Scale Speaker Diarization of Radio Broadcast Archives
Emre Yılmaz, Adem Derinel, Zhou Kun +4
This paper describes our initial efforts to build a large-scale speaker diarization (SD) and identification system on a recently digitized radio broadcast archive from the Netherla…
Multi-Graph Decoding for Code-Switching ASR
Emre Yılmaz, Samuel Cohen, Xianghu Yue +2
In the FAME! Project, a code-switching (CS) automatic speech recognition (ASR) system for Frisian-Dutch speech is developed that can accurately transcribe the local broadcaster's b…
Code-Switching Detection Using ASR-Generated Language Posteriors
Qinyi Wang, Emre Yılmaz, Adem Derinel +1
Code-switching (CS) detection refers to the automatic detection of language switches in code-mixed utterances. This task can be achieved by using a CS automatic speech recognition…