Robust language identification using convolutional neural network features

Ganapathy, Sriram; Han, Ki Jin; Thomas, Samuel; Omar, Mohamed Kamal; Segbroeck, Maarten Van; Narayanan, Shrikanth

doi:10.21437/interspeech.2014-419

Cited by 27 publications

(5 citation statements)

References 16 publications

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“…These approaches required significant domain knowledge [4,16]. Nowadays most of the attempts on spoken language identification rely on neural networks for meaningful feature extraction and classification [15,7].…”

Section: Related Workmentioning

confidence: 99%

Spoken Language Identification Using ConvNets

Sarthak

Shukla

Mittal

2019

Lecture Notes in Computer Science

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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 widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a language is present or an explicit one where text is available with its corresponding transcript. This paper focuses on an implicit approach due to the absence of transcriptive data. This paper benchmarks existing models and proposes a new attention based model for language identification which uses log-Mel spectrogram images as input. We also present the effectiveness of raw waveforms as features to neural network models for LI tasks. For training and evaluation of models, we classified six languages (English, French, German, Spanish, Russian and Italian) with an accuracy of 95.4% and four languages (English, French, German, Spanish) with an accuracy of 96.3% obtained from the VoxForge dataset. This approach can further be scaled to incorporate more languages.

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Section: Related Workmentioning

confidence: 99%

Spoken Language Identification Using ConvNets

Sarthak

Shukla

Mittal

2019

Lecture Notes in Computer Science

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“…Use of i-vectors requires significant domain knowledge (Dehak et al 2011b;Martínez et al 2011). In recent trends, researchers rely on neural networks for feature extraction and classification (Lopez-Moreno et al 2014;Ganapathy et al 2014). Researcher Revay and Teschke (2019) used the ResNet50 (He et al 2016) framework for classifying languages by generating the log-Mel spectra for each raw audio.…”

Section: Related Workmentioning

confidence: 99%

“…This work is characterised by a modified training strategy that provides equal class distribution and efficient memory utilisation. Ganapathy et al (2014) reported how they used bottleneck features from a CNN for the LID task. Bottleneck features were used in conjunction with conventional acoustic features, and performance was evaluated.…”

Section: Related Workmentioning

confidence: 99%

Is Attention always needed? A case study on language identification from speech

Mandal,

Pal,

Dutta

et al. 2024

Nat. lang. processing

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Language identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior to utilisation. The LID task assumes a significant role in scenarios where ASR systems are unable to comprehend the spoken language in multilingual settings, leading to unsuccessful speech recognition outcomes. The present study introduces convolutional recurrent neural network (CRNN)-based LID, designed to operate on the mel-frequency cepstral coefficient (MFCC) characteristics of audio samples. Furthermore, we replicate certain state-of-the-art methodologies, specifically the convolutional neural network (CNN) and Attention-based convolutional recurrent neural network (CRNN with Attention), and conduct a comparative analysis with our CRNN-based approach. We conducted comprehensive evaluations on thirteen distinct Indian languages, and our model resulted in over 98 per cent classification accuracy. The LID model exhibits high-performance levels ranging from 97 per cent to 100 per cent for languages that are linguistically similar. The proposed LID model exhibits a high degree of extensibility to additional languages and demonstrates a strong resistance to noise, achieving 91.2 per cent accuracy in a noisy setting when applied to a European Language (EU) dataset.

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“…The background model can be a Gaussian mixture model (GMM) [14] or a DNN model [11], [15]. The i-vectors extracted from the training data are used to train classifiers such as support vector machines (SVMs) which perform the task of language identification [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Towards Relevance and Sequence Modeling in Language Recognition

Padi¹,

Mohan²,

Ganapathy

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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The task of automatic language identification (LID) involving multiple dialects of the same language family in the presence of noise is a challenging problem. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the temporal sequence of the speech signal. The conventional approaches to LID (and for speaker recognition) ignore the sequence information by extracting long-term statistical summary of the recording assuming an independence of the feature frames. In this paper, we propose a neural network framework utilizing short-sequence information in language recognition. In particular, a new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. This relevance weighting is achieved using the bidirectional long short-term memory (BLSTM) network with attention modeling. We explore two approaches, the first approach uses segment level i-vector/x-vector representations that are aggregated in the neural model and the second approach where the acoustic features are directly modeled in an end-to-end neural model. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data as well as in the RATS language recognition corpus. In these experiments on noisy LRE tasks as well as the RATS dataset, the proposed approach yields significant improvements over the conventional i-vector/x-vector based language recognition approaches as well as with other previous models incorporating sequence information.

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Robust language identification using convolutional neural network features

Cited by 27 publications

References 16 publications

Spoken Language Identification Using ConvNets

Spoken Language Identification Using ConvNets

Is Attention always needed? A case study on language identification from speech

Towards Relevance and Sequence Modeling in Language Recognition

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