Publications related to Grapheme-based Automatic Speech Recognition using Probabilistic Lexical Modeling

Related publications (24)

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Institute of Electrical and Electronics Engineers Inc.2025

On Breathing Pattern Information in Synthetic Speech

Mathew Magimai Doss, Zohreh Mostaani

The respiratory system is an integral part of human speech production. As a consequence, there is a close relation between respiration and speech signal, and the produced speech signal carries breathing pattern related information. Speech can also be gener ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Automatic pathological speech assessment

Parvaneh Janbakhshi

Many pathologies cause impairments in the speech production mechanism resulting in reduced speech intelligibility and communicative ability. To assist the clinical diagnosis, treatment and management of speech disorders, automatic pathological speech asses ...

EPFL2022

Bertraffic: Bert-Based Joint Speaker Role And Speaker Change Detection For Air Traffic Control Communications

Petr Motlicek, Juan Pablo Zuluaga Gomez

Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech ...

IEEE2022

Efficient Transformer-Based Speech Recognition

Apoorv Vyas

Training deep neural network based Automatic Speech Recognition (ASR) models often requires thousands of hours of transcribed data, limiting their use to only a few languages. Moreover, current state-of-the-art acoustic models are based on the Transformer ...

EPFL2022

Serab: A Multi-Lingual Benchmark For Speech Emotion Recognition

Pierre Anton Beckmann

Recent developments in speech emotion recognition (SER) often leverage deep neural networks (DNNs). Comparing and benchmarking different DNN models can often be tedious due to the use of different datasets and evaluation protocols. To facilitate the proces ...

IEEE2022

Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing

Pierre Anton Beckmann

Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Subrahmanya Pavankumar Dubagunta

Speech signal conveys several kinds of information such as a message, speaker identity, emotional state of the speaker and social state of the speaker. Automatic speech assessment is a broad area that refers to using automatic methods to predict human judg ...

EPFL2021

Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

Mathew Magimai Doss, Julian David Fritsch

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognitio ...

IEEE Institute of Electrical and Electronics Engineers2021

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications

Petr Motlicek

Voice communication is the main channel to exchange information between pilots and Air-Traffic Controllers (ATCos). Recently, several projects have explored the employment of speech recognition technology to automatically extract spoken key information suc ...

MDPI2021

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

Mathew Magimai Doss, Zohreh Mostaani

Respiration is an essential and primary mechanism for speech production. We first inhale and then produce speech while exhaling. When we run out of breath, we stop speaking and inhale. Though this process is involuntary, speech production involves a system ...

PERGAMON-ELSEVIER SCIENCE LTD2021

PARSINLU: A Suite of Language Understanding Challenges for Persian

Rabeeh Karimi Mahabadi

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the wid ...

2021

Towards versatile conversations with data-driven dialog management and its integration in commercial platforms

Pablo Cañas Castellanos

Conversational interfaces have recently become a ubiquitous element in both the personal sphere by easing access to services, and industrial environments by the automation of services, improved customer support and its corresponding cost savings. However, ...

2021

Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Enno Hermann

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should ...

2021

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Julian David Fritsch

Atypical aspects in speech concern speech that deviates from what is commonly considered normal or healthy. In this thesis, we propose novel methods for detection and analysis of these aspects, e.g. to monitor the temporary state of a speaker, diseases tha ...

EPFL2023

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...

EPFL2020

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

On matching data and model in LF-MMI-based dysarthric speech recognition

Enno Hermann

In light of steady progress in machine learning, automatic speech recognition (ASR) is entering more and more areas of our daily life, but people with dysarthria and other speech pathologies are left behind. Their voices are underrepresented in the trainin ...

EPFL2023

Automatic Pathological Speech Intelligibility Assessment Exploiting Subspace-Based Analyses

Hervé Bourlard, Ina Kodrasi, Parvaneh Janbakhshi

Competitive state-of-the-art automatic pathological speech intelligibility measures typically rely on regression training on a large number of features, require a large amount of healthy speech training data, or are applicable only to phonetically balanced ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Petr Motlicek, Juan Pablo Zuluaga Gomez

In this paper we propose a novel virtual simulation-pilot engine for speeding up air traffic controller (ATCo) training by integrating different state-of-the-art artificial intelligence (AI)-based tools. The virtual simulation-pilot engine receives spoken ...

MDPI2023