Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

Deep neural networks (DNNs) have become a standard component in supervised ASR,
used in both data-driven feature extraction and acoustic modelling. Supervision is typically …

Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

Measures of acoustic similarity between words or other units are critical for segmental
exemplar-based acoustic models, spoken term discovery, and query-by-example search …

Acoustic word embeddings-fixed-dimensional vector representations of variable-length
spoken word segments-have begun to be considered for tasks such as speech recognition …

This paper examines the impact of multilingual (ML) acoustic representations on Automatic
Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the …

In settings where only unlabeled speech data is available, speech technology needs to be
developed without transcriptions, pronunciation dictionaries, or language modelling text. A …

In the last years, the task of Query-by-Example Spoken Term Detection (QbE-STD), which
aims to find occurrences of a spoken query in a set of audio documents, has gained the …

This paper presents a study of spectral clustering-based approaches to acoustic segment
modeling (ASM). ASM aims at finding the underlying phoneme-like speech units and …

The task of query-by-example spoken term detection (QbE-STD) is to find a spoken query
within spoken audio data. Current state-of-the-art techniques assume zero prior knowledge …