by September 4, 2000 0 comments

Speech recognition is the hottest topic in research today. In
fact, many full-blown speech recognition applications are being implemented in
the West to increase work efficiency.

Speech recognition has evolved quite a bit over the past few
years. Initially, it used to work in discrete dictation mode, where you had to
pause between each spoken word. Today, however, it uses continuous dictation. It’s
also become smarter, with its own set of grammar rules to make out the meaning
of what’s being said.

Speech recognition uses several techniques to
“recognize” the human voice. It functions as a pipeline that converts
digital audio signals coming from the sound card to recognized speech. These
signals pass through several stages, where various mathematical and statistical
methods are applied to figure out what is actually being said.

Let’s take a look at how it works.

The voice input to the microphone goes to the sound card. The output from the sound card–digital audio–is processed using FFT (Fast Fourier Transform)–and further fine-processed using HMMs and other techniques. The built-in database is used for analyzing what’s been spoken. There’s a reverse feedback to the database at the final stage for the purpose of adaptation. The final recognized output then goes back to the CPU

The voice input to the microphone goes to the sound card. The output
from the sound card–digital audio–is processed using FFT (Fast
Fourier Transform)–and further fine-processed using HMMs and other
techniques. The built-in database is used for analyzing what’s been
spoken. There’s a reverse feedback to the database at the final stage
for the purpose of adaptation. The final recognized output then goes
back to the CPU

Sounds simple

First, you–the user–give a voice command over the
microphone, which is passed to the sound card in your system. This analog signal
is sampled 16,000 times a second and converted into digital form using a
technique called Pulse Code Modulation or PCM. This digital waveform is a stream
of amplitudes that look like a wavy line. The speech recognition software can’t
figure out anything from this stream–it first has to translate it into
something it can easily recognize. So, it converts this signal into a set of
discrete frequency bands using a technique called Windowed Fast Fourier
Transform (FFT). For this, the audio signal is further sampled every 1/100th of
a second and each sample is converted into a particular frequency. So, the
incoming stream is now a set of discrete frequency bands, in a form that can be
used by the speech recognizer.

The next stage involves recognizing these bands of
frequencies. For this, the speech recognition software has a database containing
thousands of frequencies or “phonemes”, as they’re called. A phoneme
is the smallest unit of speech in a language or dialect. The utterance of one
phoneme is different from another, such that if one phoneme replaces another in
a word, the word would have a different meaning. For example, if the
“b” in “bat” were replaced by the phoneme “r”, the
meaning would change to “rat”. The phoneme database is used to match
the audio frequency bands that were sampled. So, for example, if the incoming
frequency sounds like a “t”, the software will try and match it to the
corresponding phoneme in the database. Each phoneme is tagged with a feature
number, which is then assigned to the incoming signal.

Figuring out the right sound

If life were simple, each incoming frequency band would find
the right phoneme in the database. The software would then collate these to form
words, and your PC would understand you. Unfortunately, it isn’t that simple.
There can be so many variations in sound due to how words are spoken that it’s
almost impossible to exactly match an incoming sound to an entry in the
database. For example, the “t” in “the” sounds different
from the “t” in, say “table”. Not only that, but different
people would pronounce the same word differently. To make matters worse, the
environment also adds its own share of noise. Therefore, the software has to use
complex techniques to approximate the incoming sound and figure out which
phonemes are being used.

Training the software

One way of identifying phonemes is to “train” the
speech recognition software. In training, many variations of the same phoneme
are given, and the software analyzes each of these through statistical methods.
Let’s see how it recognizes one phoneme.

Compared to the sampling frequency of 1/100th of a second,
the duration of one phoneme is long. During this time, many frequency bands
would actually pass the speech recognizer, and each would be assigned a feature
number. So, the software uses statistics to figure out the probability of a
particular feature number appearing in a phoneme. The feature number with the
highest probability would correspond to the phoneme that you’ve spoken. This
way, it gathers data of the hundreds of variations of the same phoneme passed to
it, and approximates the right one.

Other techniques

This is just the tip of the iceberg–a small sample of how
speech recognition software recognizes sounds. There are many other complexities
involved in recognizing sound. For example, the software has to be able to judge
when a phoneme ends and the next one begins. For this, it uses a technique
called Hidden Markov Models (HMM), which is another mathematical model that uses
statistics. To figure out when speech starts and stops, a speech recognizer has
silence phonemes, which are also assigned feature numbers.

There are also some phonemes that depend upon what comes
before or after them. For example, consider two words, “see” and
“saw”. Here the vowels “ee” and “aw” intrude into
the phoneme “s”. You hear the vowels for a longer period than the
“s”. To solve this problem, speech recognition software uses
tri-phones, or phonemes produced along with the surrounding phonemes.

In another technique called pruning, for a particular speech,
the software generates several hypotheses on what could have been spoken. It
then generates scores for each hypothesis and the one with the highest score is
taken. The ones with the lower scores are “pruned” out.

This is the essence of how speech recognition works, though
there are lots of other complexities involved. The technology holds great scope
for the future.

Ankur Saxena

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