Spoken Language Recognition Systems Research Articles

A Discriminative Hierarchical PLDA-Based Model for Spoken Language Recognition

Spoken language recognition (SLR) refers to the automatic process used to determine the language present in a speech sample. SLR is an important task in its own right, for example, as a tool to analyze or categorize large amounts of multi-lingual data. Further, it is also an essential tool for selecting downstream applications in a work flow, for example, to chose appropriate speech recognition or machine translation models. SLR systems are usually composed of two stages, one where an embedding representing the audio sample is extracted and a second one which computes the final scores for each language. In this work, we approach the SLR task as a detection problem and implement the second stage as a probabilistic linear discriminant analysis (PLDA) model. We show that discriminative training of the PLDA parameters gives large gains with respect to the usual generative training. Further, we propose a novel hierarchical approach where two PLDA models are trained, one to generate scores for clusters of highly-related languages and a second one to generate scores conditional to each cluster. The final language detection scores are computed as a combination of these two sets of scores. The complete model is trained discriminatively to optimize a cross-entropy objective. We show that this hierarchical approach consistently outperforms the non-hierarchical one for detection of highly related languages, in many cases by large margins. We train our systems on a collection of datasets including over 100 languages, and test them both on matched and mismatched conditions, showing that the gains are robust to condition mismatch.

Spoken language recognition using a new conditional cascade method to combine acoustic and phonetic results

Spoken language recognition (SLR) is an identification process to detect the language of an audio file. Traditional SLR systems are mainly based on acoustic or phonetic approaches. These approaches have complementary characteristic, so fusing them may lead to improve the accuracy of final SLR system. In the phonetic-based approaches, the process of phone recognition, as its initial step, is a time consuming process, therefore it may results in a high computational cost for overall SLR system. In this paper, a new structure, named conditional cascade, is proposed to speed up the combined system of the phonetic and acoustic approaches. In the proposed structure, the phonetic approach is only used when the confidence score of the acoustic approach is not desirable. Hence, the performance of the SLR system can be improved, while its speed degradation is not significant. To calculate final confidence scores, a heuristic method is utilized based on the acoustic scores distribution for each language. The experimental results showed that the proposed conditional cascade system could decrease the classification error of target languages with an acceptable runtime.

Unsupervised crosslingual adaptation of tokenisers for spoken language recognition

Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary phonetic information. We present a study on the use of deep neural network tokenisers. Unsupervised crosslingual adaptation was performed to adapt the baseline tokeniser trained on English conversational telephone speech data to different languages. Two training and adaptation approaches, namely cross-entropy adaptation and state-level minimum Bayes risk adaptation, were tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems using the tokenisers adapted to different languages were combined using score fusion, giving 7–18% reduction in minimum detection cost function (minDCF) compared with the baseline configurations without adapted tokenisers. Analysis of results showed that the ensemble tokenisers gave diverse representation of phonemes, thus bringing complementary effects when SLR systems with different tokenisers were combined. SLR performance was also shown to be related to the quality of the adapted tokenisers.

Discriminative Boosting Algorithm for Diversified Front-End Phonotactic Language Recognition

Currently, phonotactic spoken language recognition (SLR) and acoustic SLR systems are widely used language recognition systems. Parallel phone recognition followed by vector space modeling (PPRVSM) is one typical phonotactic system for spoken language recognition. To achieve better performance, researchers assumed to extract more complementary information of the training data using phone recognizers trained for multiple language-specific phone recognizers, different acoustic models and acoustic features. These methods achieve good performance but usually compute at high computational cost and only using complementary information of the training data. In this paper, we explore a novel approach to discriminative vector space model (VSM) training by using a boosting framework to use the discriminative information of test data effectively, in which an ensemble of VSMs is trained sequentially. The effectiveness of our boosting variation comes from the emphasis on working with the high confidence test data to achieve discriminatively trained models. Our variant of boosting also includes utilizing original training data in VSM training. The discriminative boosting algorithm (DBA) is applied to the National Institute of Standards and Technology (NIST) language recognition evaluation (LRE) 2009 task and show performance improvements. The experimental results demonstrate that the proposed DBA shows 1.8 %, 11.72 % and 15.35 % relative reduction for 30s, 10s and 3s test utterances in equal error rate (EER) than baseline system.

Homogenous ensemble phonotactic language recognition based on SVM supervector reconstruction

Abstract Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR system. Phonotactic SLR subsystems may vary in the acoustic features vectors or include multiple language-specific phone recognizers and different acoustic models. These methods achieve good performance but usually compute at high computational cost. In this paper, a new diversification for phonotactic language recognition systems is proposed using vector space models by support vector machine (SVM) supervector reconstruction (SSR). In this architecture, the subsystems share the same feature extraction, decoding, and N-gram counting preprocessing steps, but model in a different vector space by using the SSR algorithm without significant additional computation. We term this a homogeneous ensemble phonotactic language recognition (HEPLR) system. The system integrates three different SVM supervector reconstruction algorithms, including relative SVM supervector reconstruction, functional SVM supervector reconstruction, and perturbing SVM supervector reconstruction. All of the algorithms are incorporated using a linear discriminant analysis-maximum mutual information (LDA-MMI) backend for improving language recognition evaluation (LRE) accuracy. Evaluated on the National Institute of Standards and Technology (NIST) LRE 2009 task, the proposed HEPLR system achieves better performance than a baseline phone recognition-vector space modeling (PR-VSM) system with minimal extra computational cost. The performance of the HEPLR system yields 1.39%, 3.63%, and 14.79% equal error rate (EER), representing 6.06%, 10.15%, and 10.53% relative improvements over the baseline system, respectively, for the 30-, 10-, and 3-s test conditions.

Spoken Language Recognition With Prosodic Features

Speech prosody is believed to carry much language-specific information that can be used for spoken language recognition (SLR). In the past, the use of prosodic features for SLR has been studied sporadically and the reported performances were considered unsatisfactory. In this paper, we exploit a wide range of prosodic attributes for large-scale SLR tasks. These attributes describe the multifaceted variations of F0, intensity and duration in different spoken languages. Prosodic attributes are modeled by the bag of n-grams approach with support vector machine (SVM) as in the conventional phonotactic SLR systems. Experimental results on OGI and NIST-LRE tasks showed that the use of proposed attributes gives significantly better SLR performance than those previously reported. The full feature set includes 87 prosodic attributes and redundancy among attributes may exist. Attributes are broken down into particular bigrams called bins. Four entropy-based feature selection metrics with different selection criteria are derived. Attributes can be selected by individual bins, or by attributes as batches of bins. It can also be done in a language-dependent or language-independent manner. By comparing different selection sizes and criteria, an optimal attribute subset comprising 5,000 bins is found by using a bin-level language-independent criterion. Feature selection reduces model size by 2.5 times and shortens the runtime by 6 times. The optimal subset of bins gives the lowest EER of 20.18% on NIST-LRE 2007 SLR task in a prosodic attribute model (PAM) system which exclusively modeled prosodic attributes. In a phonotactic-prosodic fusion SLR system, the detection cost, C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</sub> is 2.09%. The relative detection cost reduction is 23%.

Shifted-Delta MLP Features for Spoken Language Recognition

This letter presents our study of applying phoneme posterior features for spoken language recognition (SLR). In our work, phoneme posterior features are estimated from a multilayer perceptron (MLP) based phoneme recognizer, and are further processed through transformations including taking logarithm, PCA transformation, and appending shifted delta coefficients. The resulting shifted-delta MLP (SDMLP) features show similar distribution as conventional shifted-delta cepstral (SDC) features, and are more robust compared to the SDC features. Experiments on the NIST LRE2005 dataset show that the SDMLP features fit well with the state-of-the-art GMM-based SLR systems, and SDMLP features outperform SDC features significantly.

Error Corrective Fusion of Classifier Scores for Spoken Language Recognition

This paper investigates a new method for fusion of scores generated by multiple classification sub-systems that help to further reduce the classification error rate in Spoken Language Recognition (SLR). In recent studies, a variety of effective classification algorithms have been developed for SLR. Hence, it has been a common practice in the National Institute of Standards and Technology (NIST) Language Recognition Evaluations (LREs) to fuse the results from several classification sub-systems to boost the performance of the SLR systems. In this work, we introduce a discriminative performance measure to optimize the performance of the fusion of 7 language classifiers developed as IIR's submission to the 2009 NIST LRE. We present an Error Corrective Fusion (ECF) method in which we iteratively learn the fusion weights to minimize error rate of the fusion system. Experiments conducted on the 2009 NIST LRE corpus demonstrate a significant improvement compared to individual sub-systems. Comparison study is also conducted to show the effectiveness of the ECF method.