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Source Separation and Spatial Analysis. Statistical Parametric Speech Synthesis. Speech Recognition for Indian Languages. Syllabification, Rhythm, and Voice Activity Detection. Selected Topics in Neural Speech Processing. Applications in Education and Learning. Speaker Characterization and Analysis. Speech Intelligibility and Quality.

Voice Conversion and Speech Synthesis. Extracting Information from Audio. Speech Perception in Adverse Conditions. Speech and Language Analytics for Mental Health. Adjusting to Speaker, Accent, and Domain. Speech Synthesis Paradigms and Methods. Second Language Acquisition and Code-switching. Representation Learning for Emotion.

Articulatory Information, Modeling and Inversion. Speech Pathology, Depression, and Medical Applications. Source Separation from Monaural Input. Interspeech September , Hyderabad Chair: B. The face of the archrival reprinted the Menomonee Valley, a FREE investor that submitted trenchantly taught in to resolve what sent predominantly a enlightening limited Internet.

Interspeech 2018

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Suprasegmental Sounds

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The training process is similar as that used in speech recognition [41]. In the fine-tuning step, we set the learning rate to a small value, i. In the fine-tuning phase, the parameters of all layers are jointly tuned using the BP algorithm according to tied-state labels obtained by a forced-alignment process using pre-trained GMM-HMMs. The fine-tuning process is iteratively executed using the following settings: 10 epochs are used for BP fine-tuning.

The learning rate is fixed for the first 3 epochs, then halve for the remaining epochs. This extension is achieved by a simple linear transformation of several concatenated delta cepstral blocks. In addition, SDC is generally prone to distortion by language independent nuisance, such as speaker and channel variabilities, and specific content for a given utterance. However, DBF exploits long-term temporal information in input features through a non-linear transformation.

Futhermore, by taking into consideration the labeling information contained in the training corpus, the DBF is extracted with discriminative training, which is more robust to language-independent nuisance. Finally, DBF can be considered as a fusion of the middle-level representation between the high-level phonetic and low-level acoustic features.

The TV approach was first introduced in the context of speaker verification [24] and has become the state-of-the-art modeling technique both in speaker and language communities [25]. The basic DBF-TV framework is derived from our previous work [31] , and consists of two main parts, the acoustic frontend and TV modeling back-end, as shown in Figure 2. The acoustic frontend mainly consists of acoustic preprocessing and DBF extraction, as illustrated in the previous section, which transforms the multiple frames of MFCC and prosodic features into DBFs. The TV modeling back-end consists of the following phases, i-vector extraction, intersession compensation, and cosine scoring, which are described in the following paragraphs.

This system consists of two main phases, the acoustic frontend and TV modeling back-end. The classical JFA technique models both speaker and channel subspaces separately. However, the channel and speaker informations are difficult to separate [44]. To address this issue, TV approach was proposed to cover the total variability in an utterance using only one subspace [24]. Specifically, given an utterance, the GMM super-vector , which is created by stacking the mean vectors of a GMM adapted to that utterance, can be modeled as follows 8.

The training process of loading matrix is similar to the eigenvoice method [45].

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The difference is that in TV modeling, the loading matrix is estimated based on the variance information derived from all utterances. After i-vector extraction, two intersession compensation techniques are applied to remove the nuisance in i-vectors. The first is linear discriminant analysis LDA which is a popular dimension reduction method in the machine learning community.

Language Identification Using Spectral and Prosodic Features

Generally, LDA is based on the discriminative criterion that attempts to define new axes minimizing the within-class variance, while maximizing the between-class variance. The LDA projection matrix contains the eigenvectors with respect to the decreasing order of corresponding eigenvalues in decomposition. This is obtained by solving the following generalized eigenvalue problem 9. The matrices and denote the between-class variance and within-class variance, respectively. The second intersession compensation technique we used is within-class covariance normalization WCCN , which normalizes the cosine kernel between utterances with an inverse of the within-class covariance [24].

The within class covariance matrix is estimated as follows: The projection matrix is obtained through Cholesky decomposition of matrix. With the matrix and , the compensated i-vector can be obtained as After obtaining intersession compensated i-vectors, the representation of -th target language can be simply obtained by taking the mean of the corresponding i-vectors. Given a test utterances, the detection score for a target language can be estimated using the cosine similarity measure between the target i-vector and the test i-vector : As aforementioned, the DBF extractor is a part of the specially structured DNN, which is trained on the corpus with phonemes or phoneme states information.

This labeling information may not be sufficient to cover all LID corpus due to the limited phoneme set for a special language.

Two different PDBF-TV systems based on having different DBF extractors as parallel acoustic front ends, are proposed using two different fusion schemes: early fusion and late fusion. The early scheme conducts fusion at feature-level, where the feature from both DBF-TV systems are combined before classification.

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The late fusion scheme acts at a decision-level, where the outputs of the mono DBF-TV systems are integrated by the use of an averaging criteria. As shown in Figure 3 , in the early fusion scheme, the features i. After concatenation, the following process is used in the same way as in DBF-TV, including intersession compensation and cosine scoring.

In the late fusion scheme, the similarities estimated from different DBF-TV systems are averaged to form the final decision. To evaluate the effectiveness of the proposed DBF-based systems, we conducted extensive experiments using the LRE09 dataset, comprising 23 target languages, i. The training utterances for each language came from two different channels, i. It should be noted that the training data for each language is imbalanced. Languages such as English and Mandarin enjoy more than hours of data while languages such as English-Indian are represented by less than 5 hours of data.

In addition, some language data is collected from only one channel source.

Deep Bottleneck Features for Spoken Language Identification

In implementation, we limit the training data set to at most 15 hours for each target language and divide the LID corpus into two parts: a training dataset and a development dataset. For each target language, around 80 audited segments of approximately 30 s duration are used as the development dataset, the rest are used as training. The test utterances are also divided into three duration groups, i. The LRE09 dataset is very challenging in that 1 There are 23 languages, far more than in the previous evaluations.

The core test of LRE09 is the language detection task: Given a segment of speech and a hypothesized target language, determine whether the target language is spoken in the test segment or not [9]. According to the duration of the test utterance, the performance is evaluated on 30 s, 10 s and 3 s of data respectively. Three different metrics are used to assess the performance of LID, all evaluating the capabilities of one-versus-all language detection.

The first metric is the average decision cost function [9] , which is a measure of the cost of taking bad decisions. The second one is the DET curves [46] , which are used to represent the range of possible system operating points of detection systems and measure the system discrimination capability. We also compute the classical equal error rate EER as the performance measure. Using the proposed DBF extractor for front end feature vector formation, we implemented the two DBF-based acoustic systems, i.

The performance published in [25] was tested on exactly the same evaluation data set. This implies that, since they having the same acoustic frontend i. SDC , their back-end TV modelling implementations are also similar. Since we have established that the back-end TV modelling is similar in each case, this significant performance improvement is mainly due to ability of the DBF frontends.

It demonstrates that the DBF features are powerful and have good discriminative and descriptive capabilities for the LID. Despite the significant performance improvement seen, this configuration may not be optimal. In the following subsection, we therefore compare the performance of different DBF extractor configurations, and propose an optimal configuration for the LRE09 dataset. The experiments separately assess different input temporal window sizes as well as the number of hidden nodes for the DBF extractor output, in order to find an optimal configuration for the LRE09 dataset.

It is known that temporal context information plays an important role for LID performance. For SDC, extensive trials have been conducted [14] , leading to a relatively stable and optimal configuration. Taking a similar approach, we experimentally assess the performance of different temporal window size configurations for DBF extraction.

We can see that, for 30 s and 10 s test utterances, the DBF extractor configuration i. Taken overall, the configuration with window size 21 is optimal. In fact, this result coincides with the configuration of conventional SDC, i. In order to assess the effect of the number of hidden nodes at the output of the DBF extractor, we construct several experiments. Two baseline DBF extractor configurations are used, having and temporal input windows respectively since these yielded best performance for the 30 s, 10 s, and 3 s test utterances in the previous subsection.

The EER of 30 s, 10 s and 3 s test utterances are determined for each for hidden node numbers ranging from 20 to 60 with 43 being the nominal value, set to match the dimension of the input vector. The results are plotted in Figure 5. We can conclude that, for 30 s utterances, the number of hidden nodes in the test does not directly affect LID performance. For 10 s and 3 s test utterances, performance tends to improve as the number of hidden nodes increases.

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Performance improvement in those cases appears to saturate around dimension Therefore an optimal configuration is chosen: an input of with temporal window size 21, and 50 hidden nodes in the DBF output layer. This configuration can achieve an EER performance of 1.