Speech emotion recognition (SER) system is one of the most important building block in this age of technology, where, the human-computer interaction plays a very indispensable role. In this work, emotional speech samples are taken from two databases namely, Berlin emotional speech database (Emo-DB) and surrey audio-visual expressed emotion speech database (SAVEE). Three different cepstral features like mel-frequency cepstral coefficients (MFCC), human factor cepstral coefficients (HFCC) and gammatone frequency cepstral coefficients (GFCC) are extracted from the emotional speech samples. These features are used for training, validating and testing the classifier. The extracted features represent the emotional content present in the speech signal. Two classifiers namely, the feedforward backpropagation artificial neural network (FF-BP-ANN) and support vector machine (SVM) are used for developing SERs. These classifiers are trained to classify the input speech signals into any one emotion among the distinct emotional classes corresponding to anger, boredom, disgust, fear, happiness, neutral, sadness and surprise. The results corresponding to the usage of three different cepstral features in accurately recognizing the emotions from speech utterances of two databases are presented. Finally, the performance comparisons of SER systems are made with respect to features, classifiers and from existing literature.

Automatic speech recognition (ASR) may be defined as the process of recognizing the spoken string of words from acoustic speech signals. This paper presents an approach for designing an ASR system based on isolated speech word recognition using two dimensional root cepstrum (TDRC) coefficients as features. These features are extracted from speech signals corresponding to a moderate sized vocabulary of 54 words, taken from Tohoku University - Matsushita Japanese Isolated Word Database (TMW). The extracted features are used to train, validate and test a bayesian optimized k-nearest neighbor (k-NN) classifier. The bayesian optimization is used to select optimal machine learning hyperparameters that minimizes the cross-validation loss. It also minimizes over two other hyperparameters namely, the nearest neighborhood size and the distance function. Alongside with TDRC features, two-dimensional cepstrum (TDC) features are also extracted from the speech signals of same database. The obtained results are presented and the performance of these two features are analyzed using statistical hypothesis tests. The statistical hypothesis testing show that TDRC features are better than TDC features. The statistical hypothesis testing is also performed to determine the optimum values of root parameter (γ) used in TDRC feature extraction method for TMW speech corpus.

Speech emotion recognition (SER) involves identifying a speaker’s emotional state from their speech utterance. Prior research has explored various cepstral features for developing SER systems. Among these, Mel-frequency cepstral coefficients (MFCC) and gammatone cepstral coefficients (GTCC) are widely used. MFCCs and GTCCs are extracted using Mel filter banks and gammatone filter banks (GTFB), respectively. Traditionally, GTFBs are designed based on the equivalent rectangular bandwidth scale. Alternative frequency scales can be employed to design different variants of GTFBs, yielding distinct GTCC feature variants. In this paper, a novel Mel-scale-based GTFB (GTFB-M) is introduced to extract a novel variant of GTCC features, termed GTCC-M. The mathematical framework for designing GTFB-M is presented. The proposed GTFB-M together with the conventional Mel and gammatone filter banks are used to extract three types of cepstral features from emotional speech signals in the Berlin Database of Emotional Speech (Emo-DB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). To evaluate SER performance, speaker-dependent (SD) and speaker-independent (SI) SER systems are developed using support vector machines with one-versus-one and one-versus-all strategies. The effectiveness of individual and combined cepstral features is analyzed. Experimental results show that the GTCC-M features derived from GTFB-M perform comparably to traditional MFCCs and GTCCs in emotion recognition. Moreover, combining the proposed features with the conventional cepstral features enhances the overall SER performance. In the case of the Emo-DB database, standalone GTCC-M features achieves recognition (testing) accuracies of 80.12% (SD) and 63.03% (SI). The best recognition (testing) accuracies of 88.82% (SD) and 79.17% (SI) are achieved using an optimal feature combination, i.e., MFCC + GTCC + GTCC-M, when compared to the independent use of MFCC and GTCC features. Similarly, for the RAVDESS database, standalone GTCC-M features achieves recognition (testing) accuracies of 61.84% (SD) and 40.83% (SI). The best recognition (testing) accuracies of 78.62% (SD) and 46.88% (SI) are achieved using an optimal feature combination, i.e., MFCC + GTCC + GTCC-M, when compared to the independent use of MFCC and GTCC features. These findings underscore the potential of GTCC-M features to improve the SER performance when integrated with the MFCC and GTCC features.

‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx^® Vivado^™ tool. The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail.