Spoken language identification using deep learning
The process of detecting language from an audio clip by an unknown speaker, regardless of
gender, manner of speaking, and distinct age speaker, is defined as spoken language …
gender, manner of speaking, and distinct age speaker, is defined as spoken language …
The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods
The variety of accents has posed a big challenge to speech recognition. The Accented
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …
An overview of Indian spoken language recognition from machine learning perspective
Automatic spoken language identification (LID) is a very important research field in the era of
multilingual voice-command-based human-computer interaction. A front-end LID module …
multilingual voice-command-based human-computer interaction. A front-end LID module …
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition
of accented speech still remains a dominant problem. In order to create more inclusive ASR …
of accented speech still remains a dominant problem. In order to create more inclusive ASR …
Convolutional neural network based language identification system: A spectrogram based approach
Identifying the language spoken in an audio source is the difficult task of automatic language
identification (LID) in speech processing. Short audio segments pose a significant challenge …
identification (LID) in speech processing. Short audio segments pose a significant challenge …
Spoken language identification system for kashmiri and related languages using mel-spectrograms and deep learning approach
Language identification, being the front-end for various natural language processing tasks,
plays an important role in language translation. Owing to this, the focus has been on the field …
plays an important role in language translation. Owing to this, the focus has been on the field …
[PDF][PDF] End to end spoken language diarization with Wav2vec embeddings
The performance of the available end-to-end (E2E) spoken language diarization (LD)
systems is biased towards primary language. This is due to the unavailability of sufficient …
systems is biased towards primary language. This is due to the unavailability of sufficient …
[PDF][PDF] First workshop on speech processing for code-switching in multilingual communities: Shared task on code-switched spoken language identification
Code-switched speech and language processing is challenging due to the paucity of
publicly-available datasets for research. We describe a shared task on language …
publicly-available datasets for research. We describe a shared task on language …
The Effect of Synthetic Voice Data Augmentation on Spoken Language Identification on Indian Languages
Multilingual based voice activated human computer interaction systems are currently in high
demand. The Spoken Language Identification Unit (SPLID) is an inevitable front end unit of …
demand. The Spoken Language Identification Unit (SPLID) is an inevitable front end unit of …
Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario
Abstract Domain mismatch is a critical issue when it comes to spoken language
identification. To overcome the domain mismatch problem, we have applied several …
identification. To overcome the domain mismatch problem, we have applied several …