<HTML
><HEAD
><TITLE
>Inside Speech Recognition</TITLE
><META
NAME="GENERATOR"
CONTENT="Modular DocBook HTML Stylesheet Version 1.76b+
"><LINK
REL="HOME"
TITLE="Speech Recognition HOWTO"
HREF="index.html"><LINK
REL="PREVIOUS"
TITLE="Speech Recognition Software"
HREF="software.html"><LINK
REL="NEXT"
TITLE="Publications"
HREF="publications.html"></HEAD
><BODY
CLASS="SECT1"
BGCOLOR="#FFFFFF"
TEXT="#000000"
LINK="#0000FF"
VLINK="#840084"
ALINK="#0000FF"
><DIV
CLASS="NAVHEADER"
><TABLE
SUMMARY="Header navigation table"
WIDTH="100%"
BORDER="0"
CELLPADDING="0"
CELLSPACING="0"
><TR
><TH
COLSPAN="3"
ALIGN="center"
>Speech Recognition HOWTO</TH
></TR
><TR
><TD
WIDTH="10%"
ALIGN="left"
VALIGN="bottom"
><A
HREF="software.html"
ACCESSKEY="P"
>Prev</A
></TD
><TD
WIDTH="80%"
ALIGN="center"
VALIGN="bottom"
></TD
><TD
WIDTH="10%"
ALIGN="right"
VALIGN="bottom"
><A
HREF="publications.html"
ACCESSKEY="N"
>Next</A
></TD
></TR
></TABLE
><HR
ALIGN="LEFT"
WIDTH="100%"></DIV
><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="INSIDE">6. Inside Speech Recognition</H1
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="RECOGNIZERS">6.1. How Recognizers Work</H2
><P
>
Recognition systems can be broken down into two main types. Pattern
Recognition systems compare patterns to known/trained patterns to
determine a match. Acoustic Phonetic systems use knowledge of the
human body (speech production, and hearing) to compare speech features
(phonetics such as vowel sounds). Most modern systems focus on the
pattern recognition approach because it combines nicely with current
computing techniques and tends to have higher accuracy.</P
><P
>Most recognizers can be broken down into the following steps:</P
><P
> <P
></P
><OL
TYPE="1"
><LI
><P
> Audio recording and Utterance detection
</P
></LI
><LI
><P
> Pre-Filtering (pre-emphasis, normalization, banding, etc.)
</P
></LI
><LI
><P
> Framing and Windowing (chopping the data into a usable format)
</P
></LI
><LI
><P
> Filtering (further filtering of each window/frame/freq. band)
</P
></LI
><LI
><P
> Comparison and Matching (recognizing the utterance)
</P
></LI
><LI
><P
> Action (Perform function associated with the recognized pattern)
</P
></LI
></OL
></P
><P
>
Although each step seems simple, each one can involve a multitude of
different (and sometimes completely opposite) techniques. </P
><P
>(1) Audio/Utterance Recording: can be accomplished in a number of ways.
Starting points can be found by comparing ambient audio levels (acoustic
energy in some cases) with the sample just recorded. Endpoint detection
is harder because speakers tend to leave "artifacts" including
breathing/sighing,teeth chatters, and echoes. </P
><P
>(2) Pre-Filtering: is accomplished in a variety of ways, depending on
other features of the recognition system. The most common methods are
the "Bank-of-Filters" method which utilizes a series of audio filters to
prepare the sample, and the Linear Predictive Coding method which uses
a prediction function to calculate differences (errors). Different
forms of spectral analysis are also used.</P
><P
>(3) Framing/Windowing involves separating the sample data into
specific sizes. This is often rolled into step 2 or step 4. This step
also involves preparing the sample boundaries for analysis (removing
edge clicks, etc.)</P
><P
>(4) Additional Filtering is not always present. It is the final
preparation for each window before comparison and matching. Often this
consists of time alignment and normalization.</P
><P
>There are a huge number of techniques available for (5), Comparison
and Matching. Most involve comparing the current window with known
samples. There are methods that use Hidden Markov Models (HMM),
frequency analysis, differential analysis, linear algebra
techniques/shortcuts, spectral distortion, and time distortion methods.
All these methods are used to generate a probability and accuracy match.</P
><P
>(6) Actions can be just about anything the developer wants. *GRIN*</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="DIGITALAUDIO">6.2. Digital Audio Basics</H2
><P
>Audio is inherently an analog phenomenon. Recording a digital sample
is done by converting the analog signal from the microphone to an
digital signal through the A/D converter in the sound card. When a
microphone is operating, sound waves vibrate the magnetic element in
the microphone, causing an electrical current to the sound card (think
of a speaker working in reverse). Basically, the A/D converter records
the value of the electrical voltage at specific intervals.</P
><P
>There are two important factors during this process. First is the
"sample rate", or how often to record the voltage values. Second, is
the "bits per sample", or how accurate the value is recorded. A third
item is the number of channels (mono or stereo), but for most ASR
applications mono is sufficient. Most applications use pre-set values
for these parameters and user's shouldn't change them unless the
documentation suggests it. Developers should experiment with different
values to determine what works best with their algorithms.</P
><P
>So what is a good sample rate for ASR? Because speech is relatively
low bandwidth (mostly between 100Hz-8kHz), 8000 samples/sec (8kHz) is
sufficient for most basic ASR. But, some people prefer 16000
samples/sec (16kHz) because it provides more accurate high frequency
information. If you have the processing power, use 16kHz. For most
ASR applications, sampling rates higher than about 22kHz is a waste.</P
><P
>And what is a good value for "bits per sample"? 8 bits per sample
will record values between 0 and 255, which means that the position
of the microphone element is in one of 256 positions. 16 bits per
sample divides the element position into 65536 possible values.
Similar to sample rate, if you have enough processing power and
memory, go with 16 bits per sample. For comparison, an audio
Compact Disc is encoded with 16 bits per sample at about 44kHz.</P
><P
>The encoding format used should be simple - linear signed or
unsigned. Using a U-Law/A-Law algorithm or some other compression
scheme is usually not worth it, as it will cost you in computing power,
and not gain you much.</P
></DIV
></DIV
><DIV
CLASS="NAVFOOTER"
><HR
ALIGN="LEFT"
WIDTH="100%"><TABLE
SUMMARY="Footer navigation table"
WIDTH="100%"
BORDER="0"
CELLPADDING="0"
CELLSPACING="0"
><TR
><TD
WIDTH="33%"
ALIGN="left"
VALIGN="top"
><A
HREF="software.html"
ACCESSKEY="P"
>Prev</A
></TD
><TD
WIDTH="34%"
ALIGN="center"
VALIGN="top"
><A
HREF="index.html"
ACCESSKEY="H"
>Home</A
></TD
><TD
WIDTH="33%"
ALIGN="right"
VALIGN="top"
><A
HREF="publications.html"
ACCESSKEY="N"
>Next</A
></TD
></TR
><TR
><TD
WIDTH="33%"
ALIGN="left"
VALIGN="top"
>Speech Recognition Software</TD
><TD
WIDTH="34%"
ALIGN="center"
VALIGN="top"
> </TD
><TD
WIDTH="33%"
ALIGN="right"
VALIGN="top"
>Publications</TD
></TR
></TABLE
></DIV
></BODY
></HTML
>
