Developing Acoustic Models for Automatic Speech Recognition in Swedish

• URL: http://arxiv.org/abs/2404.16547v1
• Date: Thu, 25 Apr 2024 12:03:14 GMT
• Title: Developing Acoustic Models for Automatic Speech Recognition in Swedish
• Authors: Giampiero Salvi,
• Abstract summary: This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish.
• Score: 6.5458610824731664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done employing hidden Markov models and using the SpeechDat database to train their parameters. Acoustic modeling has been worked out at a phonetic level, allowing general speech recognition applications, even though a simplified task (digits and natural number recognition) has been considered for model evaluation. Different kinds of phone models have been tested, including context independent models and two variations of context dependent models. Furthermore many experiments have been done with bigram language models to tune some of the system parameters. System performance over various speaker subsets with different sex, age and dialect has also been examined. Results are compared to previous similar studies showing a remarkable improvement.

Related papers

• A Suite for Acoustic Language Model Evaluation [20.802090523583196]
  We introduce SALMon, a novel evaluation suite encompassing background noise, emotion, speaker identity and room impulse response. We evaluate several speech language models on SALMon, thus highlighting the strengths and weaknesses of each evaluated method.
  arXiv  Detail & Related papers  (2024-09-11T17:34:52Z)
• 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)
• Self-Supervised Models of Speech Infer Universal Articulatory Kinematics [44.27187669492598]
  We show "inference of articulatory kinematics" as fundamental property of SSL models. We also show that this abstraction is largely overlapping across the language of the data used to train the model. We show that with simple affine transformations, Acoustic-to-Articulatory inversion (AAI) is transferrable across speakers, even across genders, languages, and dialects.
  arXiv  Detail & Related papers  (2023-10-16T19:50:01Z)
• Disentangling Voice and Content with Self-Supervision for Speaker Recognition [57.446013973449645]
  This paper proposes a disentanglement framework that simultaneously models speaker traits and content variability in speech. It is validated with experiments conducted on the VoxCeleb and SITW datasets with 9.56% and 8.24% average reductions in EER and minDCF.
  arXiv  Detail & Related papers  (2023-10-02T12:02:07Z)
• Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study [68.88536866933038]
  Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies. Recent investigations proposed the use of discrete speech units derived from self-supervised learning representations. Applying various methods, such as de-duplication and subword modeling, can further compress the speech sequence length.
  arXiv  Detail & Related papers  (2023-09-27T17:21:13Z)
• MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training [74.32603591331718]
  We propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attain state-of-the-art (SOTA) overall scores.
  arXiv  Detail & Related papers  (2023-05-31T18:27:43Z)
• Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
  Modelling of early language acquisition aims to understand how infants bootstrap their language skills. Recent developments have enabled the use of more naturalistic training data for computational models. It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
  arXiv  Detail & Related papers  (2023-05-03T08:25:37Z)
• Ensemble knowledge distillation of self-supervised speech models [84.69577440755457]
  Distilled self-supervised models have shown competitive performance and efficiency in recent years. We performed Ensemble Knowledge Distillation (EKD) on various self-supervised speech models such as HuBERT, RobustHuBERT, and WavLM. Our method improves the performance of the distilled models on four downstream speech processing tasks.
  arXiv  Detail & Related papers  (2023-02-24T17:15:39Z)
• A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement [20.329872147913584]
  We compare different methods of incorporating phonetic information in a speech enhancement model. We observe the influence of different phonetic content models as well as various feature-injection techniques on enhancement performance.
  arXiv  Detail & Related papers  (2022-06-22T12:00:50Z)
• Deep Learning Based Assessment of Synthetic Speech Naturalness [14.463987018380468]
  We present a new objective prediction model for synthetic speech naturalness. It can be used to evaluate Text-To-Speech or Voice Conversion systems.
  arXiv  Detail & Related papers  (2021-04-23T16:05:20Z)
• Data-driven Detection and Analysis of the Patterns of Creaky Voice [13.829936505895692]
  Creaky voice is a quality frequently used as a phrase-boundary marker. The automatic detection and modelling of creaky voice may have implications for speech technology applications.
  arXiv  Detail & Related papers  (2020-05-31T13:34:30Z)

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.