The Norwegian Parliamentary Speech Corpus

• URL: http://arxiv.org/abs/2201.10881v1
• Date: Wed, 26 Jan 2022 11:41:55 GMT
• Title: The Norwegian Parliamentary Speech Corpus
• Authors: Per Erik Solberg and Pablo Ortiz
• Abstract summary: The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. Training on the NPSC is shown to have a "democratizing" effect in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.
• Score: 0.5874142059884521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a "democratizing" effect in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.

Related papers

• Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis [30.97784092953007]
  This paper investigates the use of unsupervised text-to-speech synthesis (TTS) as a data augmentation method to improve accented speech recognition. TTS systems are trained with a small amount of accented speech training data and their pseudo-labels rather than manual transcriptions. This approach enables the use of accented speech data without manual transcriptions to perform data augmentation for accented speech recognition.
  arXiv  Detail & Related papers  (2024-07-04T16:42:24Z)
• An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
  This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
  arXiv  Detail & Related papers  (2024-06-13T08:16:52Z)
• Speech collage: code-switched audio generation by collaging monolingual corpora [50.356820349870986]
  Speech Collage is a method that synthesizes CS data from monolingual corpora by splicing audio segments. We investigate the impact of generated data on speech recognition in two scenarios.
  arXiv  Detail & Related papers  (2023-09-27T14:17:53Z)
• DiariST: Streaming Speech Translation with Speaker Diarization [53.595990270899414]
  We propose DiariST, the first streaming ST and SD solution. It is built upon a neural transducer-based streaming ST system and integrates token-level serialized output training and t-vector. Our system achieves a strong ST and SD capability compared to offline systems based on Whisper, while performing streaming inference for overlapping speech.
  arXiv  Detail & Related papers  (2023-09-14T19:33:27Z)
• Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation [76.13334392868208]
  Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues. In this work, we explore self-supervised pre-training with unlabeled speech data and data augmentation to tackle this issue.
  arXiv  Detail & Related papers  (2022-04-06T17:59:22Z)
• Finnish Parliament ASR corpus - Analysis, benchmarks and statistics [11.94655679070282]
  The Finnish parliament is the largest publicly available collection of manually transcribed speech data for Finnish with over 3000 hours of speech and 449 speakers. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. We develop a complete Kaldi-based data preparation pipeline, and hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and attention-based encoder-decoder (AED) ASR recipes.
  arXiv  Detail & Related papers  (2022-03-28T16:29:49Z)
• Textless Speech-to-Speech Translation on Real Data [49.134208897722246]
  We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language. We tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data.
  arXiv  Detail & Related papers  (2021-12-15T18:56:35Z)
• Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models [55.82292352607321]
  Code-switching (CS) is common in daily conversations where more than one language is used within a sentence. This paper uses the recently successful self-supervised learning (SSL) methods to leverage many unlabeled speech data without CS.
  arXiv  Detail & Related papers  (2021-10-07T14:43:35Z)
• English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System [3.4888132404740797]
  We evaluate a state-of-the-art automatic speech recognition model, using unseen data from a corpus with a wide variety of labeled English accents. We show that there is indeed an accuracy bias in terms of accentual variety, favoring the accents most prevalent in the training corpus.
  arXiv  Detail & Related papers  (2021-05-09T08:24:33Z)
• FT Speech: Danish Parliament Speech Corpus [21.190182627955817]
  This paper introduces FT Speech, a new speech corpus created from the recorded meetings of the Danish Parliament. 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.
  arXiv  Detail & Related papers  (2020-05-25T19:51:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.