18 accepted papers
Decoding selective auditory attention from electroencephalography (EEG) signals has gained considerable interest. However, few studies have looked into tracking the dynamic trajectory of moving sound source in complex auditory environments, e.g. with multiple moving speakers. We propose a novel mode…
This paper presents a zero-shot voice cloning system leveraging the DIS-Vector framework, which disentangles and encodes key speech features: content, pitch, timbre, and rhythm. Using the YourTTS architecture, the system synthesizes high-quality speech with precise control over both speaker identity…
Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can lev…
Visual Grounding (VG) methods in Visual Question Answering (VQA) attempt to improve VQA performance by strengthening a model’s reliance on question-relevant visual information. The presence of such relevant information in the visual input is typically assumed in training and testing. This assumption…
Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem among state-of-the-art VQA systems and can manifest in over-re…
This work is based on the participation by the HyperAttention team in the Auditory EEG Decoding Challenge, 2023 (ICASSP 2023 Signal Processing Grand Challenge) task 1, which deals with the match-mismatch classification of speech stimuli and EEG responses of human listeners. We demonstrate the benefi…
Electromyographic (EMG) signals of articulatory muscles reflect the speech production process even if the user is speaking silently i.e. moving the articulators without producing audible sound. We propose Speech-Unit-based EMG-to-Speech (SU-E2S), a system which relies on EMG to synthesize speech whi…
Audio has been increasingly used as a novel digital phenotype that carries important information of the subject’s health status. We can find tremendous efforts given to this young and promising field, i.e., computer audition for healthcare (CA4H), whereas the application scenarios have not been full…
Target language extraction (TLE) is a novel task in the field of selective auditory attention, which seeks to extract all speech signals that are spoken in a target language from other sources in a multilingual cocktail party. In our prior studies, a TLE model was trained to extract a predefined, si…
In this work, we present a qualitative and quantitative analysis of speech and language features derived from two different corpora with the aim to predict early signs of dementia. One corpus consists of the Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) designed to inves…
Dealing with Out Of Vocabulary (OOV) words or unseen words is one of the main issues of Machine Translation (MT) as well as automatic speech recognition (ASR) systems. For morphologically rich languages having high type token ratio, the OOV percentage is also quite high. Sub-word segmentation has be…
Neurological disorders can severely impact speech communication. Recently, neural speech prostheses have been proposed that reconstruct intelligible speech from neural signals recorded superficially on the cortex. Thus far, it has been unclear whether similar reconstruction is feasible from deeper b…
The End-to-End (E2E) approach, which maps a sequence of input features into a sequence of graphemes or words, to Automatic Speech Recognition (ASR) is a hot research agenda. It is interesting for less-resourced languages since it avoids the use of pronunciation dictionary, which is one of the major…
This paper describes new reference benchmark results based on hybrid Hidden Markov Model and Deep Neural Networks (HMM-DNN) for the GlobalPhone (GP) multilingual text and speech database. GP is a multilingual database of high-quality read speech with corresponding transcriptions and pronunciation di…
In this work, we present speech recognition systems for four Ethiopian languages: Amharic, Tigrigna, Oromo and Wolaytta. We have used comparable training corpora of about 20 to 29 hours speech and evaluation speech of about 1 hour for each of the languages. For Amharic and Tigrigna, lexical and lang…
The Everyday Activities Science and Engineering (EASE) Collaborative Research Consortium’s mission to enhance the performance of cognition-enabled robots establishes its foundation in the EASE Human Activities Data Analysis Pipeline. Through collection of diverse human activity information resources…
Zero-resource Automatic Speech Recognition (ZR ASR) addresses target languages without given pronunciation dictionary, transcribed speech, and language model. Lexical discovery for ZR ASR aims to extract word-like chunks from speech. Lexical discovery benefits from the availability of written transl…
In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parame…