The performance of automatic speech recognition (ASR) systems has advanced
substantially in recent years, particularly for languages for which a large amount of …

Variation in speech is often quantified by comparing phonetic transcriptions of the same
utterance. However, manually transcribing speech is time-consuming and error prone. As an …

Response tokens (also known as backchannels, continuers, or feedback) are a frequent
feature of human interaction, where they serve to display understanding and streamline turn …

Self-supervised learning enables the training of large neural models without the need for
large, labeled datasets. It has been generating breakthroughs in several fields, including …

Phonetic forced alignment can greatly expedite spoken language analysis by providing
automatictimealignmentsattheword-andphone-levels. Inthecaseoflow-resourcelanguages, it …

Self-supervision has emerged as an effective paradigm for learning representations of
spoken language from raw audio without explicit labels or transcriptions. Self-supervised …

Many archival recordings of speech from endangered languages remain unannotated and
inaccessible to community members and language learning programs. One bottleneck is the …

Acoustic word embeddings (AWEs) are vector representations such that different acoustic
exemplars of the same word are projected nearby in the embedding space. In addition to …

Despite huge improvements in automatic speech recognition (ASR) employing neural
networks, ASR systems still suffer from a lack of robustness and generalizability issues due …

Abstract In Transformer-based Speech-to-Text (S2T) translation, an encoder-decoder model
is trained end-to-end to take as input an untranscribed acoustic signal in the source …