99 research outputs found
Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board
One of the most difficult speech recognition tasks is accurate recognition of human-to-human communication. Advances in deep learning over the last few years have produced major speech improvements in recognition on the representative Switch-board conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This raises two issues - what is human performance, and how far down can we still drive speech recognition error rates? The main objective of this article is the development of a comparative study of the performance of Automatic Speech Recognition (ASR) algorithms using a database made up of a set of signals created by female and male speakers of different ages. We will also develop techniques for the Software and Hardware implementation of these algorithms and test them in an embedded electronic card based on a reconfigurable circuit (Field Programmable Gate Array FPGA). We will present an analysis of the results of classifications for the best Support Vector Machine architectures (SVM) and Artificial Neural Networks of Multi-Layer Perceptron (MLP). Following our analysis, we created NIOSII processors and we tested their operations as well as their characteristics. The characteristics of each processor are specified in this article (cost, size, speed, power consumption and complexity). At the end of this work, we physically implemented the architecture of the Mel Frequency Cepstral Coefficients (MFCC) extraction algorithm as well as the classification algorithm that provided the best results
In Car Audio
This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Profile hidden Markov models (pHMMs) are widely employed in various
bioinformatics applications to identify similarities between biological
sequences, such as DNA or protein sequences. In pHMMs, sequences are
represented as graph structures. These probabilities are subsequently used to
compute the similarity score between a sequence and a pHMM graph. The
Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these
probabilities to optimize and compute similarity scores. However, the
Baum-Welch algorithm is computationally intensive, and existing solutions offer
either software-only or hardware-only approaches with fixed pHMM designs. We
identify an urgent need for a flexible, high-performance, and energy-efficient
HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm
for pHMMs.
We introduce ApHMM, the first flexible acceleration framework designed to
significantly reduce both computational and energy overheads associated with
the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in
the Baum-Welch algorithm by 1) designing flexible hardware to accommodate
various pHMM designs, 2) exploiting predictable data dependency patterns
through on-chip memory with memoization techniques, 3) rapidly filtering out
negligible computations using a hardware-based filter, and 4) minimizing
redundant computations.
ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and
27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch
algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations
in three key bioinformatics applications: 1) error correction, 2) protein
family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x -
1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency
by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC
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