# US 2013/0159371 A1 · Arithmetic Logic Unit Architecture

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- Number: US 2013/0159371 A1
- Document ID: US20130159371A1
- Status: Published application
- Kind: A1
- Application number: 13/490,129
- Filed: 2012-06-06
- Published: 2013-06-20
- Earliest claimed: 2011-12-19
- Assignees: Spansion LLC
- Inventors: Richard Fastow; Jens Olson; Ben Michael Shoham
- Web page: https://patentgrep.com/patent/US20130159371A1

## CPC

- G06F 17/10: Complex mathematical operations {(function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544)}
- G10L 15/08: Speech classification or search
- G10L 15/28: Constructional details of speech recognition systems
- G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit
- G10L 15/14: using statistical models, e.g. Hidden Markov Models [HMMs] (G10L15/18 takes precedence)
- G10L 15/187: Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING

## Abstract

Embodiments of the present invention include an apparatus, method, and system for acoustic modeling. In an embodiment, an arithmetic logic unit for computing a one-dimensional score between a feature vector and a Gaussian probability distribution vector is provided. The arithmetic logic unit includes a computational logic unit configured to compute a first value based on a mean value and a variance value associated with a dimension of the Gaussian probability distribution vector and a dimension of a feature vector, a look up table module configured to output a second value based on the variance value, and a combination module configured to combine the first value and the second value to generate the one-dimensional score.

## Claims

### Claim 1

An arithmetic logic unit for computing a one-dimensional score between a feature vector and a Gaussian probability distribution vector, comprising:
a computational logic unit configured to compute a first value based on a mean value and a variance value associated with a dimension of the Gaussian probability distribution vector and a dimension of a feature vector;
a look up table module configured to output a second value based on the variance value; and
a combination module configured to combine the first value and the second value to generate the one-dimensional score.

### Claim 2 (depends on 1)

The arithmetic logic unit of claim 1, wherein the first value and the second value are represented using a same number of bits.

### Claim 3 (depends on 1)

The arithmetic logic unit of claim 1, wherein the computational logic unit comprises a subtraction module configured to compute the difference between the one-dimension value of the feature vector and the mean value and wherein the difference is positive.

### Claim 4 (depends on 3)

The arithmetic logic unit of claim 3, wherein the computational logic unit further comprises a look up table configured to output a third value based on the variance value.

### Claim 5 (depends on 4)

The arithmetic logic unit of claim 4, wherein the computational logic unit is configured to compute the first value as a product of a square of the difference and the third value.

### Claim 6 (depends on 1)

The arithmetic logic unit of claim 1, wherein the computational logic unit comprises an exception handling module configured to output the first value for specific values of the mean value, the variance value, and the dimension of the feature vector.

### Claim 7 (depends on 1)

The arithmetic logic unit of claim 1, wherein the computational logic unit comprises a feature vector transformation module configured to scale the dimension of the feature vector by a scaling factor.

### Claim 8 (depends on 1)

The arithmetic logic unit of claim 1, wherein the combination module comprises:
an adder configured to compute a sum of the first and second values; and
a shift module configured to shift the sum such that 0 bits are present right of a decimal point of the sum.

### Claim 9 (depends on 1)

The arithmetic logic unit of claim 1, wherein the lookup table module comprises a look up table configured to output a value lutij given by
lutij=(ln(varij)C)×M1,
where:
varij, is the variance value of the ith dimension of the jth Gaussian probability distribution vector; and
M1 is a scaling factor; and
C is a constant

### Claim 10 (depends on 1)

The arithmetic logic unit of claim 1, wherein the frst value is given by:
(
(
x
i
-
μ
ij
)
×
(
1
var
ij
)
)
2
×
M
2
,
where
xi is the value of the ith feature vector dimension,
μij is the mean value of the ith dimension of the jth Gaussian probability distribution vector;
varij, is the variance value of the ith dimension of the jth Gaussian probability distribution vector; and
M2 is a weighting factor.

### Claim 11

A method of computing a one-dimensional distance score between a feature vector and a Gaussian probability distribution vector, comprising:
computing a first value based on a mean value and a variance value associated with a dimension of the Gaussian probability distribution vector and a dimension of a feature vector;
retrieving a second value based on the variance value from a look up table module; and
combining the first value and the second value to generate the one-dimensional score.

### Claim 12 (depends on 11)

The method of claim 11, wherein the computing comprises:
computing a difference between the dimension of the feature vector and the mean value.

### Claim 13 (depends on 11)

The method of claim 11, wherein the computing comprises retrieving a third value from a second look up table based on the variance value.

### Claim 14 (depends on 11)

The method of claim 11, wherein the computing comprises computing the first value as a product of a square of the difference and the third value.

### Claim 15 (depends on 11)

The method of claim 11, further comprising, before the computing, transforming the value of the feature vector module for that dimension using a scaling factor.

### Claim 16 (depends on 11)

The method of claim 11, wherein the lookup table module comprises a look up table configured to output a value lutij given by
lutij=(ln(varij)−C)×M1,
where:
varij, is the variance value of the ith dimension of the jth Gaussian probability distribution vector:
M1 is a scaling factor; and
C is a constant,

### Claim 17 (depends on 11)

The method of claim 11, wherein the first value is given by:
(
(
x
i
-
μ
ij
)
×
(
1
var
ij
)
)
2
×
M
2
,
where:
xi is the value of the ith feature vector dimension,
μij is the mean value of the ith dimension of the jth Gaussian probability distribution vector;
varij, is the variance value of the ith dimension of the jth Gaussian probability distribution vector; and
M2 is a weighting factor.