1,049 research outputs found
Transducer-based language embedding for spoken language identification
The acoustic and linguistic features are important cues for the spoken
language identification (LID) task. Recent advanced LID systems mainly use
acoustic features that lack the usage of explicit linguistic feature encoding.
In this paper, we propose a novel transducer-based language embedding approach
for LID tasks by integrating an RNN transducer model into a language embedding
framework. Benefiting from the advantages of the RNN transducer's linguistic
representation capability, the proposed method can exploit both
phonetically-aware acoustic features and explicit linguistic features for LID
tasks. Experiments were carried out on the large-scale multilingual LibriSpeech
and VoxLingua107 datasets. Experimental results showed the proposed method
significantly improves the performance on LID tasks with 12% to 59% and 16% to
24% relative improvement on in-domain and cross-domain datasets, respectively.Comment: This paper was submitted to Interspeech 202
MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization
To enhance the reliability and robustness of language identification (LID)
and language diarization (LD) systems for heterogeneous populations and
scenarios, there is a need for speech processing models to be trained on
datasets that feature diverse language registers and speech patterns. We
present the MERLIon CCS challenge, featuring a first-of-its-kind Zoom video
call dataset of parent-child shared book reading, of over 30 hours with over
300 recordings, annotated by multilingual transcribers using a high-fidelity
linguistic transcription protocol. The audio corpus features spontaneous and
in-the-wild English-Mandarin code-switching, child-directed speech in
non-standard accents with diverse language-mixing patterns recorded in a
variety of home environments. This report describes the corpus, as well as LID
and LD results for our baseline and several systems submitted to the MERLIon
CCS challenge using the corpus.Comment: Accepted by Interspeech 2023, 5 pages, 2 figures, 3 table
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
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