The ability of artificial intelligence (AI) systems to perceive and comprehend audio signals is
crucial for many applications. Although significant progress has been made in this area …

Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

Neural audio codecs are initially introduced to compress audio data into compact codes to
reduce transmission latency. Researchers recently discovered the potential of codecs as …

Recently, large pretrained language models have demonstrated strong language
understanding capabilities. This is particularly reflected in their zero-shot and in-context …

We introduce a new cross-modal fusion technique designed for generative error correction
in automatic speech recognition (ASR). Our methodology leverages both acoustic …

Large language models (LLMs) have gained considerable attention for Artificial Intelligence
Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct …

Humans are surrounded by audio signals that include both speech and non-speech sounds.
The recognition and understanding of speech and non-speech audio events, along with a …

Advances in machine learning have made it possible to perform various text and speech
processing tasks, such as automatic speech recognition (ASR), in an end-to-end (E2E) …

Text language models have shown remarkable zero-shot capability in generalizing to
unseen tasks when provided with well-formulated instructions. However, existing studies in …

Recent studies have demonstrated promising outcomes by employing large language
models with multi-tasking capabilities. They utilize prompts to guide the model's behavior …