By Priyabrata Sinha
ISBN-10: 0387755802
ISBN-13: 9780387755809
ISBN-10: 0387755810
ISBN-13: 9780387755816
Speech Processing has quickly emerged as probably the most common and well-understood software parts within the broader self-discipline of electronic sign Processing. in addition to the telecommunications functions that experience hitherto been the biggest clients of speech processing algorithms, numerous non-traditional embedded processor purposes are bettering their performance and person interfaces by using a variety of elements of speech processing.
Speech Processing in Embedded Systems describes a number of parts of speech processing, and a few of the algorithms and criteria that deal with each one of those parts. the themes coated comprise kinds of Speech Compression, Echo Cancellation, Noise Suppression, Speech acceptance and Speech Synthesis. furthermore this publication explores a number of matters and issues on the topic of effective implementation of those algorithms on real-time embedded structures, together with the function performed by means of processor CPU and peripheral functionality.
This quantity is ideal for engineers keen on the layout and implementation of speech processing applications.
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Extra resources for Speech Processing in Embedded Systems
Sample text
The implications of Absolute Threshold for speech processing algorithms are obvious: by ignoring (or at least reducing the weight given to) frequencies outside this sensitive region, the amount of information needed to represent a given speech can be reduced. On the contrary, more emphasis can be given to accurately representing the speech data within the region in which the human ear is more sensitive. As noted above, this depends on the particular listener, but some advantage may be derived from knowledge of average human auditory capabilities.
In other words, this is nothing but a Vector Dot Product operation. As we will see in Chap. 4, this is the most fundamental and common mathematical operation in DSP algorithms, and DSP/DSC processor architectures attempt to optimize this computation to the fullest. 17), only feed-forward terms: each output data sample only depends on a certain number of past input samples (depending on the filter length) and there is no dependence on past outputs. This characteristic makes the FIR Filter operation inherently stable (from a mathematical standpoint) and less prone to being affected by the constraints of quantization in a processor.
Finally, and most importantly, we will explore some popular speech processing building-block techniques that enable us to extract critical pieces of information from the speech signal, such as which category a speech segment belongs to, the pitch of the sound, and the energy contained therein. , the human vocal system. Speech is basically generated when sound pressure waves (typically originating from air pushed out by the lungs) are channeled or restricted in various ways by manual control of the anatomical components of the human vocal system.