Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks
Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently
outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks …
outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks …
A unified deep neural network for speaker and language recognition
Learned feature representations and sub-phoneme posteriors from Deep Neural Networks
(DNNs) have been used separately to produce significant performance gains for speaker …
(DNNs) have been used separately to produce significant performance gains for speaker …
Grey wolf optimization-extreme learning machine for automatic spoken language identification
Natural language classification and determination based on a particular content and dataset
is carried out using Spoken Language Identification (LID) which typically involves the …
is carried out using Spoken Language Identification (LID) which typically involves the …
Progress of machine learning based automatic phoneme recognition and its prospect
M Malakar, RB Keskar - Speech Communication, 2021 - Elsevier
A phoneme is the smallest perceptually distinct sound unit that can be distinguished among
words in a particular language. Every language has its own set of phonemes, and all …
words in a particular language. Every language has its own set of phonemes, and all …
Spoken language identification based on particle swarm optimisation–extreme learning machine approach
The determination and classification of natural language based on specified content and
data set involves a process known as spoken language identification (LID). To initiate the …
data set involves a process known as spoken language identification (LID). To initiate the …
A deep dive into deep learning techniques for solving spoken language identification problems
Automatic language identification has always been a challenging issue and an important
research area in speech signal processing. It is the process of identifying a language from a …
research area in speech signal processing. It is the process of identifying a language from a …
Multiclass language identification using deep learning on spectral images of audio signals
S Revay, M Teschke - arxiv preprint arxiv:1905.04348, 2019 - arxiv.org
The first step in any voice recognition software is to determine what language a speaker is
using, and ideally this process would be automated. The technique described in this paper …
using, and ideally this process would be automated. The technique described in this paper …
Blind channel codes recognition via deep learning
This paper considers the blind recognition of the type and the encoding parameters of
channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural …
channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural …
[PDF][PDF] An End-to-End Deep Learning Framework for Speech Emotion Recognition of Atypical Individuals.
D Tang, J Zeng, M Li - Interspeech, 2018 - researchgate.net
The goal of the ongoing ComParE 2018 Atypical Affect subchallenge is to recognize the
emotional states of atypical individuals. In this work, we present three modeling methods …
emotional states of atypical individuals. In this work, we present three modeling methods …
Spoken language identification using convnets
Abstract Language Identification (LI) is an important first step in several speech processing
systems. With a growing number of voice-based assistants, speech LI has emerged as a …
systems. With a growing number of voice-based assistants, speech LI has emerged as a …