An Efficient Optimal Reconstruction Based Speech Separation Based on Hybrid Deep Learning Technique

Conventional single-channel speech separation has two long-standing issues. The first issue, over-smoothing, is addressed, and estimated signals are used to expand the training data set. Second, DNN generates prior knowledge to address the problem of incomplete separation and mitigate speech distortion. To overcome all current issues, we suggest employing an efficient optimal reconstruction-based speech separation (ERSS) to overcome those problems using a hybrid deep learning technique. First, we propose an integral fox ride optimization (IFRO) algorithm for spectral structure reconstruction with the help of multiple spectrum features: time dynamic information, binaural and mono features. Second, we introduce a hybrid retrieval-based deep neural network (RDNN) to reconstruct the spectrograms size of speech and noise directly. The input signals are sent to Short Term Fourier Transform (STFT). STFT converts a clean input signal into spectrograms then uses a feature extraction technique called IFRO to extract features from spectrograms. After extracting the features, using the RDNN classification algorithm, the classified features are converted to softmax. ISTFT then applies to softmax and correctly separates speech signals. Experiments show that our proposed method achieves the highest gains in SDR, SIR, SAR STIO, and PESQ outcomes of 10.9, 15.3, 10.8, 0.08, and 0.58, respectively. The Joint-DNN-SNMF obtains 9.6, 13.4, 10.4, 0.07, and 0.50, comparable to the Joint-DNN-SNMF. The proposed result is compared to a different method and some previous work. In comparison to previous research, our proposed methodology yields better results

Low Resource Efficient Speech Retrieval

Speech retrieval refers to the task of retrieving the information, which is useful or relevant to a user query, from speech collection. This thesis aims to examine ways in which speech retrieval can be improved in terms of requiring low resources - without extensively annotated corpora on which automated processing systems are typically built - and achieving high computational efficiency. This work is focused on two speech retrieval technologies, spoken keyword retrieval and spoken document classification. Firstly, keyword retrieval - also referred to as keyword search (KWS) or spoken term detection - is defined as the task of retrieving the occurrences of a keyword specified by the user in text form, from speech collections. We make advances in an open vocabulary KWS platform using context-dependent Point Process Model (PPM). We further accomplish a PPM-based lattice generation framework, which improves KWS performance and enables automatic speech recognition (ASR) decoding. Secondly, the massive volumes of speech data motivate the effort to organize and search speech collections through spoken document classification. In classifying real-world unstructured speech into predefined classes, the wildly collected speech recordings can be extremely long, of varying length, and contain multiple class label shifts at variable locations in the audio. For this reason each spoken document is often first split into sequential segments, and then each segment is independently classified. We present a general purpose method for classifying spoken segments, using a cascade of language independent acoustic modeling, foreign-language to English translation lexicons, and English-language classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large classification performance improvements. Lastly, we remove the need of any orthographic lexicon and instead exploit alternative unsupervised approaches to decoding speech in terms of automatically discovered word-like or phoneme-like units. We show that the spoken segment representations based on such lexical or phonetic discovery can achieve competitive classification performance as compared to those based on a domain-mismatched ASR or a universal phone set ASR

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems