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When limited training data were used, the i-vector yields the best identification rate. In [ 42 ] a comparative study on spoken language identification using deep neural networks was presented by the authors. In [ 45 ], the authors reported experiments on language identification using i-vectors and conditional random fields CRF [ 46 — 49 ]. The i-vector paradigm for language identification with SVM [ 50 ] was also applied in [ 51 ]. SVM with local Fisher discriminant analysis was used in [ 52 ]. Although significant improvements in LID have been achieved using phonotactic approaches, most state-of-the-art systems still rely on acoustic modeling.

In the current study, recall, precision, F1-score and unweighted average recall UAR are used as evaluation metrics. Based on Table 1 , the metrics in binary classification case are computed as follows: 1. The metrics shown in Eq 1 can be generalized for multi-class classification by considering the individual classes, accordingly.

Bilingualism and Multilingualism from a Socio-Psychological Perspective

It contains 12 hours of audiovisual data produced by ten actors. The IEMOCAP database is annotated by multiple annotators into several categorical labels, such as anger, happiness, sadness, and neutrality, as well as dimensional labels such as valence, activation and dominance.

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In the current study, categorical labels were used to classify the emotional states of neutral , happy , angry , and sad. To avoid unbalanced data, training utterances and for testing 50 utterances randomly selected for each emotion were used. The data are annotated with 11 emotion categories by five human labelers on the word level. In the current study, the FAU Aibo data are used for classification of the angry , emphatic , joyful , neutral , and rest emotional states.

To use balanced training and test data, training utterances and test utterances randomly selected for each emotion were used. The German database used was the Berlin Emo-DB database, which includes seven emotional states: anger, boredom, disgust, anxiety, happiness, sadness, and neutral speech. The utterances were produced by ten professional German actors five female and five male uttering ten sentences with an emotionally neutral content but expressed with the seven different emotions.

The actors produced 69 frightened, 46 disgusted, 71 happy, 81 bored, 79 neutral, 62 sad, and angry emotional sentences. In the multilingual experiment on three languages, the emotions happy, neutral, sad, and angry were considered. For each emotion, 40 instances were used for training, and 22 instances were used for testing. Four professional female actors simulated Japanese emotional speech. These comprised neutral, happy, angry, and sad emotional states.

Fifty-one utterances for each emotion were produced by each speaker. The sentences were selected from a Japanese book for children. The data were recorded at 48 kHz and down-sampled to 16 kHz, and they also contained short and longer utterances varying from 1. Twenty-eight utterances from each speaker and emotion were used for training and 20 utterances from each speaker and emotion were used for testing.

In total, utterances were used for training, and utterances were used for testing.

The remaining utterances were excluded due to poor speech quality. Six emotions were considered namely, happy , angry , sad , neutral , emphatic , and rest.

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For training, utterances were used, and for testing, utterances for each emotion were used. The training and testing data included randomly selected utterances from both the English and German corpora. In the case of spoken language identification in the first-pass, the same data as that used in speech emotion recognition were used. For each language, the utterances of all emotions were pooled to create the training and test data for the language identification task.

Previous studies showed that language identification performance is improved by using SDC feature vectors, which are obtained by concatenating delta cepstra across multiple frames. The SDC features are described by the N number of cepstral coefficients, d time advance and delay, k number of blocks concatenated for the feature vector, and P time shift between consecutive blocks.

For each SDC final feature vector, kN parameters are used. In contrast, in the case of conventional cepstra and delta cepstra feature vectors, 2N parameters are used. The SDC is calculated as follows: 2. In the current study, SDC coefficients were used not only in spoken language identification, but also in emotion classification. Fig 1 shows the computation procedure of the SDC coefficients. In automatic speech recognition, speaker recognition, and language identification MFCC features are among the most popular and widely used acoustic features.

Therefore, in modeling the languages being identified, this study also used 12 MFCC features, concatenated with SDC coefficients to form feature vectors of length The MFCC features were extracted every 10 ms using a window-length of 20 ms. The extracted acoustic features were used to construct the i-vectors used in emotion and spoken language identification modeling and classification. In many studies, GMM supervectors are used as features.

The GMM supervectors are extracted by concatenating the means of the adapted model. The problem of using GMM supervectors is their high dimensionality. To address this issue, the i-vector paradigm was introduced which overcomes the limitations of high dimensionality.

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In the case of i-vectors, the variability contained in the GMM supervectors is modeled with a small number of factors, and the whole utterance is represented by a low dimensional i-vector of dimension. Considering language identification, an input utterance can be modeled as: 3 where M is the language-dependent supervector, m is the language-independent supervector, T is the total variability matrix, and w is the i-vector. Both the total variability matrix and language-independent supervector are estimated from the complete set of the training data. The same procedure is used to extract i-vectors used in speech emotion recognition.

DNN is an important method for machine learning, and has been applied in many areas. A DNN is a feed-forward neural network with many i. The main advantage of DNNs compared to shallow networks is the better feature expression and the ability to perform complex mapping. Deep learning is behind several of the most recent breakthroughs in computer vision, speech recognition, and agents that achieved human-level performance in games such as go and poker. In the current study, four hidden layers with 64 units and ReLu activation function are used.

On top, a fully-connected Softmax layer is added.

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The number of batches is set to , and epochs are used. A convolutional neural network is a special variant of the conventional deep neural network, and consists of alternating convolution and pooling layers. Convolutional neural networks have been successfully applied to sentence classification [ 53 ], image classification [ 54 ], facial expression recognition [ 55 ], and in speech emotion recognition [ 56 ]. In [ 57 ] bottleneck features for language identification are extracted using CNNs.

On top, a fully connected Softmax layer was used.

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The batch size was set to 64, and the dropout probability was set to 0. The epochs number was Fig 2 shows the architecture of the proposed method. In the first pass of the proposed method for emotion recognition, a spoken language identification module is implemented. The task of this module is to identify the spoken language and to switch to the appropriate emotion models. Although the proposed method focuses on only two languages, the system can deal with additional languages of interest.

Therefore, it is of vital importance to apply powerful classification approaches and effective feature extraction methods. To address this issue, in the current study state-of-the-art DNN and CNN, in conjunction with i-vectors features are used. As shown, when using supplemented with SDC coefficients the identification rate are is Without SDC coefficients, the rates in some cases are slightly lower.

The results show the effectiveness of using deep learning and i-vectors for spoken language identification. Note, however, that only two languages are identified and very high rates may be expected. Also the recording environment and conditions may differ resulting in higher classification rates. The problems of speaker, environment, acoustic, and technology based mismatch in speech, speaker, and language recognition have been addressed and discussed in details in [ 58 ].

In that study, the authors suggested some solutions to enable the collection of more realistic data. On the other hand, language identification using emotional data was not associated with additional difficulty compared to normal speech. In general, language identification is conducted using normal speech. In the proposed method, however, emotional speech is used to identify the language in the first pass.