Improving of Open-Set Language Identification by Using Deep SVM and Thresholding Functions
01 Oct 2017-pp 796-802
TL;DR: This paper proposes a deep SVM based LID back-end system to improve the target languages identification and defines three OOS thresholding formulations, which are used to decide whether the speech segment is a target or OOS language.
Abstract: State-of-the-art language identification (LID) systems are based on an iVector feature extractor front-end followed by a multi-class recognition back-end. Identification accuracy degrades considerably when LID systems face open-set languages. As compared to in-set identification task, the open-set task is adequate to mimic the real challenge of language identification. In this paper, we propose an approach to the problem of out-of-set (OOS) data detection in the context of open-set language identification with zero-knowledge for OOS languages. The main feature of this study is the emphasis on the in-set (target) language identification, on the one hand, and on OOS language detection, on the other hand. Accordingly, we propose a deep SVM based LID back-end system to improve the target languages identification. Along with that, we define three OOS thresholding formulations. These formulations are used to decide whether the speech segment is a target or OOS language. The experimental results demonstrate the effectiveness of the deep SVM back-end system as compared to state-of-the-art techniques. Besides that, the thresholding functions perfectly detect and reject the OOS data. A relative decrease of 6% in Equal Error Rate (EER) is reported over classical OOS detection methods, in discriminating target and OOS languages.
Citations
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TL;DR: This paper proposes to use speech signal patterns for spoken language identification, where image-based features are used and the highest accuracy of 99.96%, which outperforms the state-of-the-art reported results.
Abstract: Western countries entertain speech recognition-based applications. It does not happen in a similar magnitude in East Asia. Language complexity could potentially be one of the primary reasons behind this lag. Besides, multilingual countries like India need to be considered so that language identification (words and phrases) can be possible through speech signals. Unlike the previous works, in this paper, we propose to use speech signal patterns for spoken language identification, where image-based features are used. The concept is primarily inspired from the fact that speech signal can be read/visualized. In our experiment, we use spectrograms (for image data) and deep learning for spoken language classification. Using the IIIT-H Indic speech database for Indic languages, we achieve the highest accuracy of 99.96%, which outperforms the state-of-the-art reported results. Furthermore, for a relative decrease of 4018.60% in the signal-to-noise ratio, a decrease of only 0.50% in accuracy tells us the fact that our concept is fairly robust.
20 citations
TL;DR: This paper proposes image-based features for speech signal classification because it is possible to identify different patterns by visualizing their speech patterns and the highest accuracy of 94.51% was obtained.
Abstract: Like other applications, under the purview of pattern classification, analyzing speech signals is crucial. People often mix different languages while talking which makes this task complicated. This happens mostly in India, since different languages are used from one state to another. Among many, Southern part of India suffers a lot from this situation, where distinguishing their languages is important. In this paper, we propose image-based features for speech signal classification because it is possible to identify different patterns by visualizing their speech patterns. Modified Mel frequency cepstral coefficient (MFCC) features namely MFCC- Statistics Grade (MFCC-SG) were extracted which were visualized by plotting techniques and thereafter fed to a convolutional neural network. In this study, we used the top 4 languages namely Telugu, Tamil, Malayalam, and Kannada. Experiments were performed on more than 900 hours of data collected from YouTube leading to over 150000 images and the highest accuracy of 94.51% was obtained.
8 citations
Cites methods from "Improving of Open-Set Language Iden..."
...[29] used deep SVM for detecting out of set languages in the task of language identification and presented 3 formulations for the out of set languages as well....
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TL;DR: Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation and language must be identified before speech recognition as such...
Abstract: Speech recognition in multilingual scenario is not trivial in the case when multiple languages are used in one conversation. Language must be identified before we process speech recognition as such...
7 citations
TL;DR: This work tackles the open-set task by adapting two modern-day state-of-the-art approaches to closed-set language identification: the first using a CRNN with attention and the second using a TDNN.
Abstract: While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when it fails to match any of our known language options. We tackle the open-set task by adapting two modern-day state-of-the-art approaches to closed-set language identification: the first using a CRNN with attention and the second using a TDNN. In addition to enhancing our input feature embeddings using MFCCs, log spectral features, and pitch, we will be attempting two approaches to out-of-set language detection: one using thresholds, and the other essentially performing a verification task. We will compare both the performance of the TDNN and the CRNN, as well as our detection approaches.
TL;DR: In this article , the authors proposed a semi-open set approach for the spoken dialect recognition task, where a closed set model is exposed to unknown class inputs and utterances from other unknown classes are also included.
References
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Proceedings Article•
01 Jan 2002TL;DR: Two GMM-based approaches to language identification that use shifted delta cepstra (SDC) feature vectors to achieve LID performance comparable to that of the best phone-based systems are described.
Abstract: Published results indicate that automatic language identification (LID) systems that rely on multiple-language phone recognition and n-gram language modeling produce the best performance in formal LID evaluations. By contrast, Gaussian mixture model (GMM) systems, which measure acoustic characteristics, are far more efficient computationally but have tended to provide inferior levels of performance. This paper describes two GMM-based approaches to language identification that use shifted delta cepstra (SDC) feature vectors to achieve LID performance comparable to that of the best phone-based systems. The approaches include both acoustic scoring and a recently developed GMM tokenization system that is based on a variation of phonetic recognition and language modeling. System performance is evaluated on both the CallFriend and OGI corpora.
459 citations
"Improving of Open-Set Language Iden..." refers background in this paper
...Several systems have demonstrated the effectiveness of ivector representation over the low-level acoustic features such as Mel-frequency cepstral coefficients (MFCC), and shifteddelta cepstral coefficient (SDC) [2]–[4]....
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...It consistently outperforms its high-level counterparts, including Gaussian mixture models (GMM) [2], [4], [11] and Gaussian Mixture Model-Universal Background Model (GMM-UBM) [2], [12]....
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27 Aug 2011
TL;DR: In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification and various techniques are employed to extract the most salient features in the lower dimensional i-vector space.
Abstract: In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-processing or backend techniques. Additional performance gains are observed when the system is combined with other acoustic systems.
438 citations
TL;DR: The proposed VSM approach leads to a discriminative classifier backend, which is demonstrated to give superior performance over likelihood-based n-gram language modeling (LM) backend for long utterances.
Abstract: We propose a novel approach to automatic spoken language identification (LID) based on vector space modeling (VSM). It is assumed that the overall sound characteristics of all spoken languages can be covered by a universal collection of acoustic units, which can be characterized by the acoustic segment models (ASMs). A spoken utterance is then decoded into a sequence of ASM units. The ASM framework furthers the idea of language-independent phone models for LID by introducing an unsupervised learning procedure to circumvent the need for phonetic transcription. Analogous to representing a text document as a term vector, we convert a spoken utterance into a feature vector with its attributes representing the co-occurrence statistics of the acoustic units. As such, we can build a vector space classifier for LID. The proposed VSM approach leads to a discriminative classifier backend, which is demonstrated to give superior performance over likelihood-based n-gram language modeling (LM) backend for long utterances. We evaluated the proposed VSM framework on 1996 and 2003 NIST Language Recognition Evaluation (LRE) databases, achieving an equal error rate (EER) of 2.75% and 4.02% in the 1996 and 2003 LRE 30-s tasks, respectively, which represents one of the best results reported on these popular tasks
248 citations
Proceedings Article•
06 Dec 2010TL;DR: It is demonstrated that linear MKL regularised with the p-norm squared, or with certain Bregman divergences, can indeed be trained using SMO, and the resulting algorithm retains both simplicity and efficiency and is significantly faster than state-of-the-art specialised p- norm MKL solvers.
Abstract: Our objective is to train p-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, easy to implement and adapt, and efficiently scales to large problems. As a result, it has gained widespread acceptance and SVMs are routinely trained using SMO in diverse real world applications. Training using SMO has been a long standing goal in MKL for the very same reasons. Unfortunately, the standard MKL dual is not differentiable, and therefore can not be optimised using SMO style co-ordinate ascent. In this paper, we demonstrate that linear MKL regularised with the p-norm squared, or with certain Bregman divergences, can indeed be trained using SMO. The resulting algorithm retains both simplicity and efficiency and is significantly faster than state-of-the-art specialised p-norm MKL solvers. We show that we can train on a hundred thousand kernels in approximately seven minutes and on fifty thousand points in less than half an hour on a single core.
190 citations
Proceedings Article•
01 Jan 2003
TL;DR: This paper describes and evaluates three techniques that have been applied to the language identification problem: phone recognition, Gaussian mixture modeling, and support vector machine classification and an approach to dealing with the problem of out-of-set data.
Abstract: Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three techniques that have been applied to the language identification problem: phone recognition, Gaussian mixture modeling, and support vector machine classification. A recognizer that fuses the scores of three systems that employ these techniques produces a 2.7% equal error rate (EER) on the 1996 NIST evaluation set and a 2.8% EER on the NIST 2003 primary condition evaluation set. An approach to dealing with the problem of out-of-set data is also discussed.
176 citations
"Improving of Open-Set Language Iden..." refers background or methods in this paper
...This approach is straightforward and fast for application and in contrast to existing works [18], [20], [21], does not rely on the use of additional data from OOS languages which may not be available....
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...For instance, in [20]–[23], non-target languages data are pooled from different resources to build OOS corpus, which can be costly and time-consuming....
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...[20] NIST LRE 1996&2003 OOS modeling GMM, SVM and tokenizer Campbell et al....
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