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Found 52 papers, 5 papers with code
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR
Many factors have separately shown their effectiveness on improving multilingual ASR.
Creating Personalized Synthetic Voices from Articulation Impaired Speech Using Augmented Reconstruction Loss
Experimental results on a real case of tongue cancer patient confirm that the synthetic voice achieves comparable articulation quality to unimpaired natural speech, while effectively maintaining the target speaker's individuality.
Modeling Intrapersonal and Interpersonal Influences for Automatic Estimation of Therapist Empathy in Counseling Conversation
Counseling is usually conducted through spoken conversation between a therapist and a client.
A Study on Prosodic Entrainment in Relation to Therapist Empathy in Counseling Conversation
Counseling is carried out as spoken conversation between a therapist and a client.
Efficient Black-Box Speaker Verification Model Adaptation with Reprogramming and Backend Learning
The development of deep neural networks (DNN) has significantly enhanced the performance of speaker verification (SV) systems in recent years.
Sparsely Shared LoRA on Whisper for Child Speech Recognition
Child speech, as a representative type of low-resource speech, is leveraged for adaptation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
CoMFLP: Correlation Measure based Fast Search on ASR Layer Pruning
The search process is carried out in two steps: (1) coarse search: to determine top $K$ candidates by pruning the most redundant layers based on the correlation matrix; (2) fine search: to select the best pruning proposal among $K$ candidates using a task-specific evaluation metric.
ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading
Experiments show that ContextSpeech significantly improves the voice quality and prosody expressiveness in paragraph reading with competitive model efficiency.
Creating Personalized Synthetic Voices from Post-Glossectomy Speech with Guided Diffusion Models
Specifically, a diffusion-based speech synthesis model is trained on original recordings, to capture and preserve the target speaker's original articulation style.
Learning Representation of Therapist Empathy in Counseling Conversation Using Siamese Hierarchical Attention Network
Conversations with extreme values of empathy rating are used to train a Siamese network based encoder with contrastive loss.
Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data
One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training.
Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring
Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i. e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation.
An ASR-free Fluency Scoring Approach with Self-Supervised Learning
A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Model Compression for DNN-based Speaker Verification Using Weight Quantization
Experimental results on VoxCeleb show that weight quantization is effective for compressing SV models.
Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verification
The weights are imposed on the input features to improve the representation ability for speaker modeling.
iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre
The emotion encoder extracts the identity of emotion type as well as the respective emotion intensity from the mel-spectrogram of input speech.
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis.
Transport-Oriented Feature Aggregation for Speaker Embedding Learning
Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling.
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification
This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels.
Learnable Frequency Filters for Speech Feature Extraction in Speaker Verification
There is no reason to expect that these features are optimal for all different tasks, including speaker verification (SV).
Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes
In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification.
MEMD-HHT based Emotion Detection from EEG using 3D CNN
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection.
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech
The performance of child speech recognition is generally less satisfactory compared to adult speech due to limited amount of training data.
CorrectSpeech: A Fully Automated System for Speech Correction and Accent Reduction
This study propose a fully automated system for speech correction and accent reduction.
Hierarchical Attention Network for Evaluating Therapist Empathy in Counseling Session
Counseling typically takes the form of spoken conversation between a therapist and a client.
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech
However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input.
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations
This paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech.
Characterizing Therapist's Speaking Style in Relation to Empathy in Psychotherapy
Using the salient utterance genres, an accuracy of $71\%$ is achieved in classifying psychotherapy sessions into ``high" and ``low" empathy level.
Acoustical Analysis of Speech Under Physical Stress in Relation to Physical Activities and Physical Literacy
The general wellness of a person is further related to his/her physical literacy (PL), which refers to a holistic description of engagement in PA.
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition
The underlying objective is to explore the feasibility of deploying LTR speech in the training of end-to-end (E2E) ASR models, as an attempt to data augmentation for improving the recognition performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Environment Aware Text-to-Speech Synthesis
The key idea is to model the acoustic environment in speech audio as a factor of data variability and incorporate it as a condition in the process of neural network based speech synthesis.
A study on the efficacy of model pre-training in developing neural text-to-speech system
However, in terms of ultimately achieved system performance for target speaker(s), the actual benefits of model pre-training are uncertain and unstable, depending very much on the quantity and text content of training data.
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph
Given the embeddings from a pair of input utterances, a graph model is designed to incorporate additional information from a group of embeddings representing the so-called auxiliary speakers.
Utterance-level neural confidence measure for end-to-end children speech recognition
The investigation is focused on evaluating and comparing the efficacies of predictor features that are derived from different internal and external modules of the E2E system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification
For a more robust ASC system, We propose a robust feature learning (RFL) framework to train the CNN.
Applying the Information Bottleneck Principle to Prosodic Representation Learning
This paper describes a novel design of a neural network-based speech generation model for learning prosodic representation. The problem of representation learning is formulated according to the information bottleneck (IB) principle.
EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion
This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness.
Detection of Consonant Errors in Disordered Speech Based on Consonant-vowel Segment Embedding
This paper investigates a neural network based approach to detecting consonant errors in disordered speech using consonant-vowel (CV) diphone segment in comparison to using consonant monophone segment.
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning
Despite the widespread utilization of deep neural networks (DNNs) for speech emotion recognition (SER), they are severely restricted due to the paucity of labeled data for training.
CUHK-EE Voice Cloning System for ICASSP 2021 M2VoC Challenge
100 and 5 utterances of 3 target speakers in different voice and style are provided in track 1 and 2 respectively, and the participants are required to synthesize speech in target speaker's voice and style.
Bayesian Learning for Deep Neural Network Adaptation
A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that is often attributable to speaker differences.
Unsupervised Spoken Term Discovery Based on Re-clustering of Hypothesized Speech Segments with Siamese and Triplet Networks
The Siamese/Triplet network is trained on the hypothesized examples to measure the similarity between two speech segments and hereby perform re-clustering of all hypothesized subword sequences to achieve spoken term discovery.
Fine-grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement
By incorporating a style predictor, the proposed system can also be used for text-to-speech synthesis.
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features
With the unsupervisedly trained acoustic models, a given audio archive is represented by a pseudo transcription, from which spoken keywords can be discovered by string mining algorithms.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Mixture factorized auto-encoder for unsupervised hierarchical deep factorization of speech signal
The USM experiments on ZeroSpeech 2017 dataset verify that the frame tokenizer is able to capture linguistic content and the utterance embedder can acquire speaker-related information.
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling
Out-of-domain ASR systems can be applied to perform speaker adaptation with untranscribed training data of the target language, and to decode the training speech into frame-level labels for DNN training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation
This study tackles unsupervised subword modeling in the zero-resource scenario, learning frame-level speech representation that is phonetically discriminative and speaker-invariant, using only untranscribed speech for target languages.
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling
Experimental results on ZeroSpeech 2017 show that both approaches are effective while the latter is more prominent, and that their combination brings further marginal improvement in across-speaker condition.
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification
Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded.
Reducing Model Complexity for DNN Based Large-Scale Audio Classification
Audio classification is the task of identifying the sound categories that are associated with a given audio signal.
Sound Audio and Speech Processing
CUHK System for QUESST Task of MediaEval 2014
This paper describes a spoken keyword search system developed at the Chinese University of Hong Kong (CUHK) for the query by example search on speech (QUESST) task of MediaEval 2014.
Ranked #3 on
Keyword Spotting
on QUESST
