Search Results for author:
Found 33 papers, 12 papers with code
Using Mixed Incentives to Document Xi’an Guanzhong
This paper describes our use of mixed incentives and the citizen science portal LanguageARC to prepare, collect and quality control a large corpus of object namings for the purpose of providing speech data to document the under-represented Guanzhong dialect of Chinese spoken in the Shaanxi province in the environs of Xi’an.
Loudspeaker Beamforming to Enhance Speech Recognition Performance of Voice Driven Applications
In this paper we propose a robust loudspeaker beamforming algorithm which is used to enhance the performance of voice driven applications in scenarios where the loudspeakers introduce the majority of the noise, e. g. when music is playing loudly.
Good practices for evaluation of machine learning systems
If the data is incorrectly selected, the wrong metric is chosen for evaluation or the significance of the comparisons between models is overestimated, conclusions may be misleading or result in suboptimal development decisions.
Self-supervised Speech Representations Still Struggle with African American Vernacular English
Underperformance of ASR systems for speakers of African American Vernacular English (AAVE) and other marginalized language varieties is a well-documented phenomenon, and one that reinforces the stigmatization of these varieties.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
As Biased as You Measure: Methodological Pitfalls of Bias Evaluations in Speaker Verification Research
Detecting and mitigating bias in speaker verification systems is important, as datasets, processing choices and algorithms can lead to performance differences that systematically favour some groups of people while disadvantaging others.
Improving child speech recognition with augmented child-like speech
State-of-the-art ASRs show suboptimal performance for child speech.
Exploring data augmentation in bias mitigation against non-native-accented speech
We aim to mitigate the bias against non-native-accented Flemish in a Flemish ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
Whispering is a distinct form of speech known for its soft, breathy, and hushed characteristics, often used for private communication.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
This pioneering effort aims to set the first benchmark for the AVTSE task, offering fresh insights into enhancing the ac-curacy of back-end speech recognition systems through AVTSE in challenging and real acoustic environments.
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems
Speech technology has improved greatly for norm speakers, i. e., adult native speakers of a language without speech impediments or strong accents.
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained models
Compared with MFCC, in the within-language scenario, the performance of these SSL speech pre-trained models on AF probing tasks achieved a maximum relative increase of 34. 4%, and it resulted in the lowest PER of 10. 2%.
Manipulation of oral cancer speech using neural articulatory synthesis
We present an articulatory synthesis framework for the synthesis and manipulation of oral cancer speech for clinical decision making and alleviation of patient stress.
Modelling word learning and recognition using visually grounded speech
We furthermore investigate whether vector quantisation, a technique for discrete representation learning, aids the model in the discovery and recognition of words.
Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
In this paper, we 1) investigate the influence of different factors (i. e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language; 2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation; and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition
In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition.
Towards Identity Preserving Normal to Dysarthric Voice Conversion
We present a voice conversion framework that converts normal speech into dysarthric speech while preserving the speaker identity.
An Objective Evaluation Framework for Pathological Speech Synthesis
The development of pathological speech systems is currently hindered by the lack of a standardised objective evaluation framework.
Pathological voice adaptation with autoencoder-based voice conversion
This approach alleviates the evaluation problem one normally has when converting typical speech to pathological speech, as in our approach, the voice conversion (VC) model does not need to be optimised for speech degradation but only for the speaker change.
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation
In the first stage, a recently proposed method in the task of unsupervised subword modeling is improved by replacing a monolingual out-of-domain (OOD) ASR system with a multilingual one to create a subword-discriminative representation that is more language-independent.
Quantifying Bias in Automatic Speech Recognition
Practice and recent evidence suggests that the state-of-the-art (SotA) ASRs struggle with the large variation in speech due to e. g., gender, age, speech impairment, race, and accents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks
Taken together, the analyses showed that the two stages in our approach are both effective in capturing phoneme and AF information.
Show and Speak: Directly Synthesize Spoken Description of Images
This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes.
How Phonotactics Affect Multilingual and Zero-shot ASR Performance
Furthermore, we find that a multilingual LM hurts a multilingual ASR system's performance, and retaining only the target language's phonotactic data in LM training is preferable.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Evaluating Automatically Generated Phoneme Captions for Images
For this, first an Image2Speech system was implemented which generates image captions consisting of phoneme sequences.
Detecting and analysing spontaneous oral cancer speech in the wild
3) We set baselines for an oral cancer speech detection task on this dataset.
Unsupervised Subword Modeling Using Autoregressive Pretraining and Cross-Lingual Phone-Aware Modeling
Our system is less sensitive to training data amount when the training data is over 50 hours.
Learning to Recognise Words using Visually Grounded Speech
We investigated word recognition in a Visually Grounded Speech model.
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages
Only a handful of the world's languages are abundant with the resources that enable practical applications of speech processing technologies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
S2IGAN: Speech-to-Image Generation via Adversarial Learning
An estimated half of the world's languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies.
Investigating the Effect of Music and Lyrics on Spoken-Word Recognition
Music stretches with and without lyrics were sampled from the same song in order to control for factors beyond the presence of lyrics.
Bayesian Models for Unit Discovery on a Very Low Resource Language
Developing speech technologies for low-resource languages has become a very active research field over the last decade.
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography.
Towards capturing fine phonetic variation in speech using articulatory features
As part of this work we are investigating automatic feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal.
