Non-uniform Region Based Features for Automatic Language Identification

In this paper, we are introducing speech database consists of 27 Indian languages for analyzing language specific information present in speech. In the context of Indian languages, systematic analysis of various speech features and classification models in view of automatic language identification has not performed, because of the lack of proper speech corpus covering majority of the Indian languages. With this motivation, we have initiated the task of developing multilingual speech corpus in Indian languages. In this paper spectral features are explored for investigating the presence of language specific information. Melfrequency cepstral coefficients (MFCCs) and linear predictive cepstral coefficients (LPCCs) are used for representing the spectral information. Gaussian mixture models (GMMs) are developed to capture the language specific information present in spectral features. The performance of language identification system is analyzed in view of speaker dependent and independent cases. The recognition performance is observed to be 96% and 45% respectively, for speaker dependent and independent environments.

With the growing demand for natural and intelligent human–machine interaction in multilingual environments, automatic language identification (LID) has emerged as a crucial component in voice-enabled systems, particularly in the automotive domain. This study proposes an audio-based LID model that identifies the spoken language directly from voice input without requiring manual language selection. The model architecture leverages two types of feature extraction pipelines: a Variational Autoencoder (VAE) and a pre-trained Wav2Vec model, both used to obtain latent speech representations. These embeddings are then fed into a multi-layer perceptron (MLP)-based classifier to determine the speaker’s language among five target languages: Korean, Japanese, Chinese, Spanish, and French. The model is trained and evaluated using a dataset preprocessed into Mel-Frequency Cepstral Coefficients (MFCCs) and raw waveform inputs. Experimental results demonstrate the effectiveness of the proposed approach in achieving accurate and real-time language detection, with potential applications in in-vehicle systems, speech translation platforms, and multilingual voice assistants. By eliminating the need for predefined language settings, this work contributes to more seamless and user-friendly multilingual voice interaction systems.

The classical language identification architecture would require a collection of languages independent text and speech information for training by the system before it can identify the languages correctly. This paper also address language identification framework however with data has been downsized considerably from general language identification architecture. The system goal is to identify the type of language being spoken to the system based on a series of trained speech with sound file features and without any language text data or lexical knowledge of the spoken language. The system is also expected to be able to be deployed in mobile platform in future. This paper is specifically about measuring the performance optimisation of audio filters on a CNN model integration for the language identification system. There are several metric to gauge the performance identification system for a classification problem. Precision, recall and F1 Scores is presented for the performance evaluation with different combination of filters together with CNN model as the framework of the language identification system. The goal is not to get the best filter for noise, instead to identify the filter that is a good fit to develop language model with environmental noise for a robust language identification system. Our experiments manage to identify the best combination of filters to increase the accuracy of language identification using short speech. This resulting us to modify our pre-processing phase in the overall language identification system.

The introduction of a phonetic engine in the literature is that it is a system which transforms a speech signal into some symbolic form. Phone recognition is a primary task of a PE. Among the various other applications of PE, one very useful application is the Language Identification (LID). This chapter discusses some issues pertaining to the use of PE in phone recognition as well as in language identification. Here, we have used PEs for three Indian Languages: Manipuri, Assamese and Bengali, in building the LID system. These languages are widely spoken in the Northeastern region of India. In the development of PEs, the International Phonetic Alphabet (IPA) symbols are used in the data transcription process. In modeling the phonetic units Hidden Markov Models (HMM) have been used in the training phase. These trained HMMs are then used in phone recognitions leading to the identification of language(s) of unknown test utterances. The overall phone recognition accuracies reported by the existing PEs for the above selected languages are \(62.11\%\) for standard Manipuri language, \(59.0\%\) for Kakching Dialect of Manipuri, \(43.28\%\) for standard Assamese and \(48.58\%\) for Bengali language. Automatic LID is possible using a set of PEs in testing unknown utterances due to the language bias of these systems. Various levels of identification rates reported in some LID tasks carried out with PEs are discussed here to look into the issues belonging to it.

This chapter describes the types of information that can be used to characterize spoken languages. Automatic spoken language identification (LID) systems, which are tasked with determining the identity of the language of speech samples, can utilize a variety of information sources in order to distinguish among languages. In this chapter, we first define what we mean by a language (as opposed to a dialect). We then describe some of the language collections that have been used to investigate spoken language identification, followed by discussion of the types of features that have been or could be utilized by automatic systems and people. In general, approaches used by people and machines differ, perhaps sufficiently to suggest building a partnership between human and machine. We finish with a discussion of the conditions under which textual materials could be used to augment our ability to characterize a spoken language.

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When compared with speech technologies in speech processing, automatic language identification is a relatively new yet difficult problem. In this paper, a language identification algorithm is provided and some experiments are conducted using OGI multi-language telephone speech corpus (OGI-TS). Then experiments results are described. It is shown that GMM-UBM is another efficient method to language identification problems.

It is significant to highlight that the demand of multilingual systems in human-computer interfaces is growing and automatic language identification (LID) is becoming increasingly important for the speech community as a fundamental tool to enrich theses systems. Research in this field has been active for several years. LID becomes also a useful resource in Multilingual Security System (MSS) providing methodologies to improve the robustness and the effectiveness of these systems. The main goal of our project is the development of Multilingual LVCSR systems in the Basque context. Nowadays, our work is oriented to the use of Basque in internet. The present work describes the development of a Language Identification (LID) system oriented to robust Multilingual Speech Recognition for under-resourced languages in the Basque context.

The paper describes initial experiments on automatic language identification with the particular aim of discriminating languages in the same language group. Subword models were built from the English, Dutch and Norwegian sections of the EUROM1 database using fully automatic segmentation based on TIMIT-derived models. Three techniques were then examined. In the first technique only acoustic differences between the phonemes of each language were used. The second technique relied on the relative frequencies of the phonemes of each language, while the third technique combined the two sources of information. The latter technique proved the best giving 97% accuracy for English vs. Dutch, and 90% across the three languages.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Utilising the known language of origin of a name can be useful when predicting the pronunciation of the name. When this language is not known, automatic language identification (LID) can be used to influence which language-specific grapheme-to-phoneme (G2P) predictor is triggered to produce a pronunciation for the name. We investigate the implications when both the LID system and the G2P system generate errors: what influence does this have on a resulting speech recognition system? We experiment with different approaches to LID-based dictionary creation and report on results in four South African languages: Afrikaans, English, Sesotho and isiZulu.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

The problem of Automatic Language Identification (ALI) is a very interesting area of research up today. The objective of this work is to propose a new approach based on acoustic parameters in order to identify six languages. These parameters must be adapted to the dynamic nature of speech in order to improve the automatic identification of languages. The experiences are led with six languages (German, English, Arabic, Spanish, French and Italian) on a corpus including recordings extracted from some conversations of various movies and some missions of the television and of radio. The results found show that the use of these new parameters can provide higher identification rate than those obtained with the best acoustic parameters previously used.

Human-Computer Interaction (HCI) depends critically on hand gesture recognition, which also provides the basis of sign language communication for those with hearing problems. In automatic sign language identification systems, however, the contribution of non-manual elements like head posture and facial emotions has sometimes been disregarded. In order to fully capture the diversity of sign language modalities and to enhance the quality of life of deaf people in the region of Ha'il, we present in this study a dual-stream convolutional neural network (TS-CNN) that separately models hand motions and head positions. We provide fusion techniques that improve feature correlation and representation in order to guarantee efficient cooperation between the two streams. Moreover, we use a Feature Enhancement Module (FEM) to enhance gloss-level prediction accuracy and fine temporal alignment. Two benchmark datasets, RWTH-PHOENIX-Weather 2014 and CSL Split II, are used to assess the TS-CNN framework and show a notable increase in recognition performance. Competitive Word Error Rates (WER) obtained by TS-CNN from experimental data validate its performance in simulating the complicated multimodal character of continuous sign language recognition.

Objective: Phones are basic sound units available in the spoken data. Languages differ among themselves due to use of different phone sets. This paper analyzes some aspects of automatic phone recognition and subsequent identification of a few languages from North Eastern region of India using Phonetic Engine (PE). Methods and Statistical Analysis: PE is a system which converts a speech sample into some symbolic form so that these symbols are capable of capturing all the information carried by the speech sample. In the development of PEs, the International Phonetic Alphabet (IPA) symbols are used in the data transcription process. In modelling the phonetic units Hidden Markov Models (HMM) have been used in the training phase. These trained HMMs are then used in phone recognitions leading to the identification of language(s) of unknown test utterances. Findings: PEs are built for three Indian Languages and one dialect, namely Manipuri, Assamese and Bengali and the Kakching dialect of Manipuri. These languages are widely spoken across the North Eastern region of India. The overall phone recognition accuracies reported by the PEs for the above selected languages are 62:11% for standard Manipuri language, 59:0% for Kakching dialect of Manipuri, 43:28% for standard Assamese and 48:58% for Bengali language. Application: Automatic LID is possible using a set of PEs in testing unknown utterances due to the language bias of these systems. Various level of identification rates reported in some LID tasks carried out with PEs are discussed here to make an analysis of the issues belonging to it.

This research paper will present an automatic Speaker Identification System using MFCC (Mel-Frequency Cepstral Coefficients) and BPNN (Back Propagation Neural Network). The objective of this work is to classify 20 speakers’ pattern and to identify each registered speaker correctly while testing with new input speech without any false identification. MFCC is used for the extraction of speech features from each speaker and BPNN is used for identification of the test speaker.&nbsp; The developed classifier model is tested with both registered and unregistered speakers and found that it successfully identifies all the registered speakers correctly and reject the intruder speakers. Scaled conjugate gradient training function is used for training the BPNN. A speech database consisting of 20 speakers is created from a group of 10 male and 10 female speakers with the same sentence spoken twice. The classification accuracy rate obtained from the classification is 92.1% and the correct identification rate obtained is 100%. Matlab simulation tool is used in this work