12 accepted papers
Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models, beginning with a layer-wise analysis and an explo…
This paper presents the Neural Harmony team’s submission to Task 1 (Person Identification) of the ICASSP 2025 EEG-Music Emotion Recognition Challenge, which aims to identify the subject from a given EEG segment. To enhance performance, we propose a novel architecture incorporating the Multiscale Con…
Infants are capable of learning language, predominantly through speech and associations, in impoverished environments—a phenomenon known as the Poverty of the Stimulus (POS). Is this ability uniquely human, as an innate linguistic predisposition, or can it be empirically learned through potential li…
Lexical tones play a crucial role in Sino-Tibetan languages. However, current phonetic fieldwork relies on manual effort, resulting in substantial time and financial costs. This is especially challenging for the numerous endangered languages that are rapidly disappearing, often compounded by limited…
This paper describes our submission to the ICASSP 2023 Signal Processing Grand Challenge (SPGC), which focuses on multilingual Alzheimer’s disease (AD) recognition through spontaneous speech. Our approaches include using a variety of acoustic features and silence-related information for AD detection…
With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversari…
Connectionist Temporal Classification (CTC) loss is commonly used in sequence learning applications. For example, in Automatic Speech Recognition (ASR) task, the training data consists of pairs of audio (input sequence) and text (output label),without temporal alignment information. Standard CTC com…
Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models. Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. Using BERT-base model as an example, this paper provides a comprehensive study on attent…
We propose enhancing Transformer language models (BERT, RoBERTa) to take advantage of pauses. Pauses play an important role in speech. In previous work we developed a method to encode pauses in transcripts for recognition of Alzheimer's disease. In this study, we extend this idea to language models.…
Two Mandarin speech corpora were used to investigate tonal realization in terms of duration and pitch. The data consist of nearly 1000 hours of speech from more than 1600 speakers. The two corpora, both developed for ASR, differ in speaking rate by approximately 25%. This provides an opportunity to…
In this study we developed a new method for automatic identification of t/d deletion. Our method achieved 94% accuracy on TIMIT and 87% on human-annotated data from Librispeech. We then conducted an analysis of t/d deletion on more than 500k tokens in Librispeech. The following results were found: (…
This paper presents an effort to classify Chinese speakers' L1 dialect regions from their L2 English speech. By applying LightGBM (a gradient boosting classifier based on decision trees) to softmax-based features from deep neural networks, our system achieved 68% accuracy on five dialect regions usi…