4 research outputs found
FT Speech: Danish Parliament Speech Corpus
This paper introduces FT Speech, a new speech corpus created from the
recorded meetings of the Danish Parliament, otherwise known as the Folketing
(FT). The corpus contains over 1,800 hours of transcribed speech by a total of
434 speakers. It is significantly larger in duration, vocabulary, and amount of
spontaneous speech than the existing public speech corpora for Danish, which
are largely limited to read-aloud and dictation data. We outline design
considerations, including the preprocessing methods and the alignment
procedure. To evaluate the quality of the corpus, we train automatic speech
recognition systems on the new resource and compare them to the systems trained
on the Danish part of Spr\r{a}kbanken, the largest public ASR corpus for Danish
to date. Our baseline results show that we achieve a 14.01 WER on the new
corpus. A combination of FT Speech with in-domain language data provides
comparable results to models trained specifically on Spr\r{a}kbanken, showing
that FT Speech transfers well to this data set. Interestingly, our results
demonstrate that the opposite is not the case. This shows that FT Speech
provides a valuable resource for promoting research on Danish ASR with more
spontaneous speech.Comment: Submitted to Interspeech 202
Analysis of phonetic transcriptions for Danish automatic speech recognition
ABSTRACT Automatic speech recognition (ASR) relies on three resources: audio, orthographic transcriptions and a pronunciation dictionary. The dictionary or lexicon maps orthographic words to sequences of phones or phonemes that represent the pronunciation of the corresponding word. The quality of a speech recognition system depends heavily on the dictionary and the transcriptions therein. This paper presents an analysis of phonetic/phonemic features that are salient for current Danish ASR systems. This preliminary study consists of a series of experiments using an ASR system trained on the DK-PAROLE corpus. The analysis indicates that transcribing e.g. stress or vowel duration has a negative impact on performance. The best performance is obtained with coarse phonetic annotation and improves performance 1% word error rate and 3.8% sentence error rate