Automatic language identification based on GMBM-UBBM

Speech, languages, and background voices are overlapped in the multi-speaker environment. The speech separation process is necessary for downstream applications like language and speaker identification and ASR tasks. A combined speech separation and language recognition approach is proposed in this paper. First, the monolingual information is extracted from a multi-speaker multilingual speech scenario, then fed the separated speech to a language identification model. The speech separation model is trained in a single-channel overlapped speech data, and the language identification model is trained in Oriental language corpus. The combined effect of these two models is evaluated in the task of language identification. When the language recognition network is fed with the audio spectrograms separated by the source separation network, the Chinese and Korean language recognition results are significantly improved in terms of accuracy and recall rate than the mixed audio spectrograms.

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.

In recent years, with the rise of human-computer interaction and the successful application of end-to-end models in the field of speech recognition, the construction of end-to-end speech recognition models has received extensive attention. Relying on the multi-task learning method and the connection between language identification and speech recognition, we proposed an end-to-end Transformer model, which is a multilingual speech recognition model integrating language identification. The model takes the speech recognition task as the main task and the language identification task as the auxiliary task. In this paper, the validity of the model is verified by using the datasets of 13 languages in the 2021 Oriental Language Recognition challenge (OLR). The experimental results show that the model constructed in this paper has a relative improvement of 37.46% in the speech recognition task compared with the baseline system proposed by the OLR organizer. The accuracy of language identification reaches 89.70 %. The results can get the fifth place in the 2021 OLR constraint track of speech recognition equally.

Forensic speaker recognition: A new method based on extracting accent and language information from short utterances

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.

This paper presents a number of experiments describing the use of machine learning algorithms and bag-of-words to the task of automatic language identification. The paper focuses on the identification of language varieties, which is a known weakness of general purpose language identification methods. This question was addressed by a number of studies in the recent years, most of them relying on character n-gram language models. In this paper, I experiment simple bag-of-words and compare the results with previously proposed n-gram-based approaches. To perform these classification experiments three algorithms were used: Multinomial Naive Bayes (MNB), Support Vector Machines (SVM) and the J48 classifier.

In many inference and learning tasks, collecting large amounts of labeled training data is time consuming and expensive, and oftentimes impractical. Thus, being able to efficiently use small amounts of labeled data with an abundance of unlabeled data—the topic of semi-supervised learning (SSL) [1]—has garnered much attention. In this paper, we look at the problem of choosing these small amounts of labeled data, the first step in a bootstrapping paradigm. Contrary to traditional active learning where an initial trained model is employed to select the unlabeled data points which would be most informative if labeled, our selection has to be done in an unsupervised way, as we do not even have labeled data to train an initial model. We propose using unsupervised clustering algorithms, in particular integrated sensing and processing decision trees (ISPDTs) [2], to select small amounts of data to label and subsequently use in SSL (e.g. transductive SVMs). In a language identification task on the CallFriend1 and 2003 NIST Language Recognition Evaluation corpora [3], we demonstrate that the proposed method results in significantly improved performance over random selection of equivalently sized training data.

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 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.

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 advancement in technology, communication between people around the world from different linguistic backgrounds is increasing gradually, resulting in the requirement of language identification services. Language identification techniques extract distinguishable information as features of a language from the speech corpora to differentiate one language from other. Without publicly available speech corpora, comparison between different techniques will not be much reliable. This paper investigates state-of-the-art features and techniques for language identification of under-resource and closely related languages, namely Pashto, Punjabi, Sindhi, and Urdu. For language identification, speech corpus is designed and collected for mentioned languages. The dataset is a read speech data collected over telephone network (mobile and landline) from different regions of Pakistan. The speech corpus is annotated at the sentence level using X-SAMPA, its orthographic transcription is also provided, and verified data are divided into training and evaluation sets. Mel-frequency cepstral coefficients and their shifted delta cepstral features are used to develop language identification system of target languages. Gaussian mixture model with universal background model (GMM-UBM)-based and I-vector-based language identification approaches are investigated. The results show that GMM-UBM is more effective than the I-vector for language identification of short duration test utterances.

An audio utterance can be identified as being spoken in a particular language by using automatic language identification (LID). Each language has its own phoneme set. Hence combination of these phonemes governed by phonotactics will help in distinguishing languages. In this work, we propose an automatic language identification system utilizing features derived from non-uniform speech regions to represent phonotac-tic differences among 4 Indian languages, namely Malayalam, Marathi, Assamese, and Kannada. For this, broad phoneme labels, namely approximant (A), closure (C), fricatives (F), nasals (N), plosive/stop (P), voiced stop (B), vowels (V), and silence (S) are obtained automatically by a broad phoneme classifier (BPC). It is a DNN-based classifier which uses hand-crafted features and Mel-frequency cepstral coefficients (MFCC). In order to automatically segment speech to smaller regions, first it is chopped at every silence regions using the labels obtained from BPC. Later, it is split again at the end of each vowel. Hence, small non-uniform regions are obtained which contain phoneme combinations that may be specific to the language of the utterance. From each region, only a fixed number of frames containing certain combination of phonemes are selected. A DNN classifier is trained using 13-dimensional MFCC features of 12 fixed frames of non-uniform regions for performing LID. An average accuracy of 97.03% is obtained for test utterances of 10 sec duration belonging to 4 languages.

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.

Ergodic, continuous-observation, hidden Markov models (HMMs) were used to perform automatic language classification and detection of speech messages. State observation probability densities were modeled as tied Gaussian mixtures. The algorithm was evaluated on four multilanguage speech databases: a three language subset of the Spoken Language Library, a three language subset of a five-language Rome Laboratory database, the 20-language CCITT database, and the ten-language OGI (Oregon Graduate Institute) telephone speech database. In general, the performance of a single state HMM (i.e., a static Gaussian mixture classifier) was comparable with that of the multistate HMMs, indicating that the sequential modeling capabilities of HMMs were not exploited.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, we explore the combination of MFCC and RCC features for Indian Language identification (LID) task. MFCC represents the vocal tract information while RCC represents the excitation source information, which are complementary to each other. We use Deep neural network (DNN) framework for end to end learning. To combine these two information we uses different fusion techniques such as Min, Max, Average and Min-Max for decision level fusion. We perform experiments on 13 Indian languages data set. We compare our results with state of art I-vector based approach. Min-max based fusion approach gives 9.64% ERR compare to 10.18% of 39D I-vector based approach.