Multistage word recognizer based on reliably detected phoneme similarity regions

The multistage word recognizer uses a word reference representation based on reliably detected peaks of phoneme similarity values. The word reference representation captures the basic features of the words by targets that describe the location and shape of stable peaks of phoneme similarity values. The first stage of the word hypothesizer represents each reference word with statistical information on the number of high similarity regions over a predefined number of time intervals. The second stage represents each word by a prototype that consists of a series of phoneme targets and global statistics, namely the average word duration and average match rate. These represent the degree of fit of the word prototype to its training data. Word recognition scores generated in the two stages are converted to dimensionless normalized values and combined by averaging for use in selecting the most probable word candidates.

BACKGROUND AND SUMMARY OF THE INVENTION 
The present invention relates generally to speech recognition. More 
particularly, the invention relates to a word recognizer having multistage 
word candidate hypothesizer. The system uses a compact speech 
representation based on regions of high phoneme similarity values. The 
processing stages of the word hypothesizer are applied in sequence to 
reduce the search space for a more computationally expensive fine match 
word recognition system. 
Speech representation by phoneme similarities has been applied in 
speaker-independent, template-based word recognition systems for their 
relative insensitivity to speaker variations. See "Speaker Independent 
Speech Recognition Method Using Training Speech from a Small Number of 
Speakers," by M. Hoshimi et al., Proc. ICASSP, Vol. I, pp. 469-472, 1992; 
"Speaker Independent Speech Recognition Method Using Phoneme Similarity 
Vector," by M. Hoshimi et al., Proc. ICSLP, Vol. 3, pp. 1915-1918, 1994; 
and "A Study of English Model Speech Method," by Y. Ohno et al., Proc. 
Acoustical Society of Japan, Spring 1995 (in Japanese). Phoneme similarity 
values are typically computed as the normalized Mahalanobis distance 
between a segment consisting of consecutive linear predictive coding (LPC) 
analysis frames and a standard phoneme template. There is an overall 
consistency in the shape of the phoneme similarity time series for a given 
word. Similar behavior is observed in the phoneme plausibility time series 
of the VINICS system, as described in "Plausibility Functions in 
Continuous Speech Recognition: The VINICS System," by Y. Gong and J. P. 
Haton, Speech Communication, Vol. 13, Oct. 1993, pp. 187-196. 
Speech recognition systems which match each input utterance to reference 
templates composed of phoneme similarity vectors, as in the model speech 
method of Hoshimi et al., cited above, have achieved high accuracies for 
small vocabulary tasks. Their reference speech representation is 
frame-based and requires a high data rate (typically 8 to 12 parameters 
every 10 to 20 milliseconds). The required frame-by-frame alignment is 
computationally costly and makes this approach unsuitable for larger 
vocabularies, especially on small hardware. Because the approach is 
computationally costly, it is not well suited to consumer product 
applications that, for cost reasons, cannot use large, powerful 
processors. 
The present invention represents a significant departure from current 
frame-based techniques. Whereas current techniques require a fixed number 
of parameters at a regular frame rate interval, the present invention 
removes this restriction through a novel compact speech representation 
based on regions of high phoneme similarity values. A multistage word 
hypothesizer is used prior to frame-by-frame alignment in order to reduce 
the search space and thereby improve real time performance. The number of 
stages in the hypothesizer, as well as the computational complexity of 
each stage, and the number of word candidates preserved at each stage can 
be adjusted to achieve desired goals of speed, memory size and recognition 
accuracy for a particular application. Unlike with conventional 
techniques, the parameters used by the hypothesizer stages are not 
required to occur at regular time intervals. 
The word hypothesizer and fine match stages of the invention share the 
initial representation of speech as a sequence of multiple phoneme 
similarity values. The word hypothesizer stages further refine this speech 
representation, to preserve only the interesting regions of high phoneme 
similarity, or features. By representing the speech as features at a lower 
data rate in the initial stages of recognition, the complexity of the 
matching procedure is greatly reduced. In effect, the hypothesizer stages 
select the most probable word candidates and thereby reducing the search 
space for the fine match procedure. 
To further improve recognition reliability the probability scores obtained 
at each stage of the word hypothesizer are combined with the scores of the 
fine match procedure in order to produce a final word decision. Because 
each of the respective stages may use a different word selection strategy, 
the probability scores produced at each stage are quasi-independent. By 
combining these quasi-independent sources of information produced at each 
step, a significant gain in accuracy is obtained. 
According to one aspect of the invention, a word recognizer for processing 
an input speech utterance is provided for a speech recognition system. The 
recognizer includes a phone model database for storing phone model speech 
data that corresponds to a plurality of phonemes. A phoneme similarity 
module, receptive of the input speech utterance, accesses the phone model 
database and produces phone similarity data indicative of the correlation 
between the input speech utterance and the phone model speech data 
corresponding to successive intervals of time. A high similarity module is 
coupled to the phone similarity module for identifying those intervals of 
time that contain phone similarity data that exceed a predetermined 
threshold. A region count hypothesizer stage, which includes a first word 
prototype database for storing similarity region count data for a 
plurality of words, is coupled to the high similarity module. The region 
count hypothesizer generates a first list of word candidates selected from 
the first word prototype database, based on similarity regions. 
A target congruence hypothesizer stage, having a second word prototype 
database for storing word prototype data corresponding to a plurality of 
predetermined words, receives the first list of word candidates from the 
region count hypothesizer stage. The target congruence hypothesizer stage 
is coupled to the high similarity module for generating a second list of 
at least one word candidate that is selected from the first list based on 
similarity regions. 
A word recognizer stage, having a word template database for storing word 
template data corresponding to a plurality of predetermined words, 
receives the second list of word candidates from the target congruence 
hypothesizer stage. The word recognizer selects the recognized word from 
the second list. 
For a more complete understanding of the invention, its objects and 
advantages, reference may be had to the following specification and to the 
accompanying drawings.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
The present invention employs a unique compact speech representation based 
on regions of high phoneme similarity values. As shown in FIG. 1, there is 
an overall consistency in the shape of the phoneme similarity time series 
for a given word. In FIG. 1 phoneme similarity time series for the word 
"hill" spoken by two speakers are compared. Although the precise wave 
shapes differ between the two speakers, the phoneme similarity data 
nevertheless exhibit regions of similarity between the speakers. Similar 
behavior is observed in the phoneme plausibility time series that has been 
described by Gong and Haton in "Plausibility Functions in Continuous 
Speech Recognition: The VINICS System," Speech Communication, Vol. 13, 
Oct. 1993, pp. 187-196. 
Conventional speech recognition systems match each input utterance to 
reference templates, such as templates composed on phoneme similarity 
vectors, as in the model speech method (MSM) of Hoshimi et al. In these 
conventional systems the reference speech representation is frame-based 
and requires a high data rate, typically 8 to 12 parameters every 10 to 20 
milliseconds. The frame-by-frame alignment that is required with these 
conventional systems is computationally costly and makes this approach 
unsuitable for larger vocabularies, especially when using small hardware. 
The present system uses a multistage word hypothesizer that is applied 
prior to a frame-by-frame alignment, in order to reduce the search space 
and to achieve real time performance improvements. As demonstrated in FIG. 
2, the number of stages in the hypothesizer, as well as the computational 
complexity of each stage and the number of word candidates preserved at 
each stage, can be adjusted to achieve desired goals of speed, memory size 
and recognition accuracy for a particular application. The word 
hypothesizer and fine match procedure share the initial representation of 
speech as a sequence of multiple phoneme similarity values. However, the 
word hypothesizer further refines this speech representation to preserve 
only the interesting regions of high phoneme similarity. Referring to FIG. 
3, the interesting regions of high phoneme similarity value are 
represented as high similarity regions. By representing the speech as 
features at a lower data rate in the initial stages of recognition, the 
complexity of the matching procedure is greatly reduced. 
The multistage word hypothesizer also employs a unique scoring procedure 
for propagating and combining the scores obtained at each stage of the 
word hypothesizer with the scores of the fine match procedure in order to 
produce a final word decision. By combining the quasi-independent sources 
of information produced at each stage, a significant gain in accuracy is 
obtained. 
The system's architecture features three distinct components that are 
applied in sequence on the incoming speech to compute the best word 
candidate. 
Referring to FIG. 4, an overview of the presently preferred system will be 
presented. The first component of the present system is a phoneme 
similarity front end 10 that converts speech signals into phoneme 
similarity time series. Speech is digitized at 8 kilohertz and processed 
by 8th order linear predictive coding (LPC) analysis to produce 8 cepstral 
coefficients every 100th of a second. Each block of 10 successive frames 
of cepstral coefficients is compared to 55 phoneme reference templates (a 
subset of the TIMIT phoneme units) to compute a vector of multiple phoneme 
similarity values. The block of analysis frames is then shifted by one 
frame at a time to produce a vector of phoneme similarity values each 
centisecond (each 100th of a second). As illustrated in FIG. 4, the 
phoneme similarity front end works in conjunction with a phone model 
database 12 that supplies the phoneme reference templates. The output of 
the phoneme similarity front end may be stored in a suitable memory for 
conveying the set of phoneme similarity time series so generated to the 
word hypothesizer stages. 
The word hypothesizer stages, depicted in FIG. 4 generally at 14, comprise 
the second major component of the system. A peak driven procedure is first 
applied on the phoneme similarity time series supplied by front end 10. 
The peak driven procedure extracts High Similarity Regions (HS Regions). 
In this process, low peaks and local peaks of phoneme similarity values 
are discarded, as illustrated in FIG. 3. In the preferred embodiment 
regions are characterized by 4 parameters: phoneme symbol, height at the 
peak location and time locations of the left and right frames. Over our 
data corpus, an average of 60 regions per second of speech is observed. In 
FIG. 4 the high similarity region extraction module 16 performs the peak 
driven procedure. The output of the HS region extraction module is 
supplied to 2 different word hypothesizer stages that operate using 
different hypothesizer techniques to provide a short list of word 
candidates for the fine match final recognizer stage 26. 
The first of the two stages of word hypothesizer 14 is the Region Count 
stage or RC stage 18. This stage extracts a short list of word candidates 
that are then supplied to the next stage of the word hypothesizer 14, the 
Target Congruence stage or TC stage 20. The RC stage 18 has an RC word 
prototype database 22 that supplies compact word representations based on 
the novel compact speech representation (regions of high phoneme 
similarity values) of the invention. Similarly, the TC stage 20 also 
includes a TC word prototype database 24 that supplies a different compact 
word representation, also based on the compact speech representation of 
the invention. The TC stage provides a more selective short list of word 
candidates, essentially a further refinement of the list produced by the 
RC stage 18. 
The fine match word recognition stage 26, the final major component of the 
present system, is preferably a fine match word recognizer that performs 
frame-by-frame alignment to select the recognized word from the short list 
supplied by TC stage 20. The word recognizer stage 26 also uses a word 
template database 28. The presently preferred word recognizer may be 
implemented according to the techniques described in "A Study of English 
Model Speech Method," by Y. Ohno et al., Proc. Acoustical Society of 
Japan, Spring 1995 (in Japanese). 
Region Count Modeling 
The RC stage 18 of word hypothesizer 14 represents each reference word with 
statistical information on the number of HS regions over a predefined 
number of time intervals. The presently preferred embodiment divides words 
into 3 equal time intervals in which each phoneme interval is described by 
(1) the mean of the number of HS regions occurring in that interval and 
(2) a weight that is inversely proportional to the variance, which 
indicates how reliable the region count is. These parameters are easily 
estimated from training data. Each word requires exactly 330 parameters, 
which corresponds to 2 statistics, each over 3 intervals each comprising 
55 phoneme units (2 statistics.times.3 intervals.times.55 phoneme units). 
Region count modeling was found to be very effective due to its fast 
alignment time (0.33 milliseconds per test word on a Sparc10 workstation) 
and its high top 10% accuracy. Note the high top 10% accuracy for the RC 
stage is graphically depicted in FIG. 6. 
The region count prototype is constructed as follows. A first utterance of 
a training word or phrase is represented as time-dependent phoneme 
similarity data. In the presently preferred embodiment each utterance is 
divided into N time intervals. Presently each utterance is divided into 3 
time intervals, with each time interval being represented by data 
corresponding to the 55 phonemes. Thus the presently preferred 
implementation represents each utterance as a 3.times.55 vector. In 
representing the utterance as a 3.times.55 vector, each vector element in 
a given interval stores the number of similarity regions that are detected 
for each given phoneme. Thus if 3 occurrences of the phoneme "ah" occur in 
the first interval, the number 3 is stored in the vector element 
corresponding t the "ah" phoneme. 
An inductive or iterative process is then performed for each of the 
successive utterances of the training word or phrase. Specifically, each 
successive utterance is represented as a vector like that of the first 
utterance. The two vectors are then combined to generate the vector sum 
and the vector sum of the squares. In addition, a scaler count value is 
maintained to keep track of the current number of utterances that have 
been combined. 
The process proceeds inductively or iteratively in this fashion, each new 
utterance being combined with the previous ones such that the sum and sum 
of squares vectors ultimately represent the accumulated data from all of 
the utterances. 
Once all training utterances have been processed in this fashion the vector 
mean and vector variance are calculated. The mean vector is calculated as 
the sum vector divided by the number of utterances used in the training 
set. The vector variance is the mean of the squares minus the square of 
the means. The mean and variance vectors are then stored as the region 
count prototype for the given word or phrase. The same procedure is 
followed to similarly produce a mean and variance vector for each of the 
remaining words or phrases in the lexicon. 
When a test utterance is compared with the RC prototype, the test utterance 
is converted into the time dependent phoneme similarity vector, 
essentially in the same way as each of the training utterances were 
converted. The Euclidean distance between the test utterance and the 
prototype is computed by subtracting the test utterance RC data vector 
from the prototype mean vector and this difference is then squared. The 
Euclidean distance is then multiplied by a weighting factor, preferably 
the reciprocal of the prototype variance. The weighted Euclidean distance, 
so calculated, is then converted into a scaler number by adding each of 
the vector component elements. In a similar fashion the weighting factor 
(reciprocal of the variance) is converted into a scaler number by adding 
all of the vector elements. The final score is then computed by dividing 
the scaler distance by the scaler weight. 
The above process may be repeated for each word in the prototype lexicon 
and the most probably word candidates are then selected based on the 
scaler score. 
Target Congruence Modeling 
The second stage of the word hypothesizer represents each reference word by 
(1) a prototype which consists of a series of phoneme targets and (2) by 
global statistics, namely the average word duration and the average "match 
rate," which represents the degree of fit of the word prototype to its 
training data. In the presently preferred embodiment targets are 
generalized HS regions described by 5 parameters: 
1. phoneme symbol; 
2. target weight (percentage occurrence in training data); 
3. average peak height (phoneme similarity value); 
4. average left frame location; 
5. average right frame location. 
Word prototypes are automatically created from the training data as 
follows. First, HS regions are extracted from the phoneme similarity time 
series for a number of training speakers. The training data may be 
generated based on speech from a plurality of different speakers or it may 
be based on multiple utterances of the same training words by a single 
speaker. Then, for each training utterance of a word, reliable HS regions 
are computed by aligning the given training utterance with all other 
utterances of the same word in the training data. This achieves 
region-to-region alignment. 
For each training utterance the number of occurrences (or probability) of a 
particular region is then obtained. At that time, regions with 
probabilities less than a pre-established Reliability Threshold (typically 
0.25) are found unreliable and are eliminated. The word prototype is 
constructed by merging reliably detected, high similarity regions to form 
targets. At the end of that process a target rate constraint (i.e. desired 
number of targets per second) is then applied to obtain a uniform word 
description level for all the words in the lexicon. The desired number of 
targets per second can be selected to meet system design constraints such 
as the ability of a given processor to handle data at a given rate. By 
controlling the target rate a reduction in the number of targets is 
achieved by keeping only the most reliable targets. Once the word 
prototype has been obtained in this fashion, the average match rate and 
average word duration are computed and stored as part of the word 
prototype data. 
The number of parameters needed to represent a word depends on the average 
duration of the word and on the level of phonetic detail that is desired. 
For a typical 500 millisecond word at 50 targets per second, the speech 
representation used by the presently preferred embodiment employs 127 
parameters, which correspond to 5 values per target.times.50 targets per 
second.times.0.5 seconds+2 global statistics (average match rate and 
average word duration). 
FIG. 5 illustrates the word prototype training procedure by which the TC 
word prototype database 24 is constructed. The RC word prototype database 
22 is constructed by similar, but far simpler process, in that only the 
presence or absence of an HS region occurring with each of the 3 equal 
time intervals must be detected. 
Referring to FIG. 5, the HS region computation module 16 is used to convert 
the similarity time series from the speech database into a list of HS 
regions. The alignment module 30 operates on this list of HS regions to 
eliminate unreliable regions by alignment across speakers. Again, the 
process can be performed across a plurality of different speakers or 
across a plurality of utterances by the same speaker. 
Next the list of reliable regions, together with the associated 
probabilities of detecting those regions is passed to the target building 
module 32. This module builds targets by unifying the region series to 
produce a list of phoneme targets associated with each word in the 
database. This list of phoneme targets is then supplied to a module 34 
that adjusts the target rate by applying the target rate constraint. The 
target rate constraint (the desired number of targets per second) may be 
set to a level that achieves the desired target rate. After adjusting the 
target rate a statistical analyzer module 36 estimates the global 
statistics (the average match rate and the average word duration) and 
these statistics along with the list of targets at the selected rate are 
then stored as the TC word prototype database 24. 
Word Hypothesization 
Given an active lexicon of N words, the region count stage is first applied 
to produce a short list of word candidates with normalized scores. A 
weighted Euclidean distance is used to measure the degree of fit of a test 
word X to a reference word P (in RC format as supplied by the RC word 
prototype database). Specifically, the weighted Euclidean distance is 
defined as 
##EQU1## 
where x.sub.ij is the number of HS regions in time interval I for phoneme 
j, where p.sub.ij is the corresponding average number of HS regions 
estimated on training data, and where w.sub.ij is the corresponding 
weight. The N/10 highest scoring word prototypes are preserved as word 
candidates and their scores (weighted Euclidean distances) are normalized 
by dividing each individual score by the highest score. This defines a 
normalized score S.sub.RC for each word. Normalized scores range from 0 to 
1 and are dimensionless, making it possible to combine scores resulting 
from different scoring methods. 
The target congruence stage is then applied on each word candidate selected 
by the RC stage. A region-to-target alignment procedure is used to produce 
a congruence score between the test word and a given word reference (in TC 
format as supplied by the TC word prototype database). The congruence 
score of a matched target CG.sub.match, that is, the alignment found 
between target t of the prototype and region r of the test word, is 
defined as 
EQU CG.sub.match (t,r)=min(A.sub.t .vertline.A.sub.r,.vertline.A.sub.r 
.vertline.A.sub.t) 
where A.sub.t and A.sub.r respectively represent the target's area and the 
aligned region's area in the time similarity plane. 
The congruence score of an unmatched target CG.sub.match is computed in the 
same way, using an estimate for the area A.sub.r of the missing HS region. 
The estimated area A.sub.r is computed as the area under the similarity 
curve for the target's phoneme label, between the projected locations of 
the target's left and right frames. 
The word congruence score is computed as the weighted sum of congruence 
scores for all the targets, divided by the sum of their weights. 
Normalized congruence scores S.sub.TC are computed by dividing the 
individual congruence scores by the highest congruence score. The final 
score output by the word hypothesizer is a combination of the information 
obtained at each hypothesizer stage. In the presently preferred embodiment 
the final score output of the hypothesizer is: 
EQU S.sub.Hypo =(S.sub.RC +S.sub.TC)/2 
In the presently preferred embodiment the five words having the highest 
combined scores are selected as word candidates for the final stage fine 
match process. 
Word Recognition 
Fine match word recognition is performed in stage 26. Unlike the word 
hypothesizer 14, the word recognizer stage 26 uses the phoneme similarity 
time series directly in a frame-by-frame, dynamic programming match on the 
list of 5 word candidates given by the hypothesizer. Fine match 
recognition scores are normalized (SFM) and are combined with the scores 
of the hypothesizer. The global score of each word in the short list is 
then defined as: 
EQU S.sub.Global =(S.sub.Hypo +S.sub.FM)/2 
Evaluation Task 
Recognition word accuracy was evaluated in isolated word 
speaker-independent mode on a speech database of 100 English proper names. 
Testing was performed in several noise conditions: clean test speech and 
speech with additive noise at 20 dB or 10 dB signal-to-noise ratio. Two 
kinds of nonstationary additive noise were used in testing: car noise, 
which was recorded in a moving Toyota Crown automobile; and data show 
noise, which was recorded in a large exhibition hall and contains 
multitalker babble and music. 
Phoneme models were trained on the TIMIT database SX sentences, downsampled 
to 8 kHz sampling rate. For training nominal clean phoneme models, each 
sentence was used twice: once as clean speech and once with artificially 
added stationary pink Gaussian noise at 20 dB SNR. (This combination was 
found to improve recognition results, even for clean test conditions). For 
training multistyle phoneme models, the additive noise was replaced by 
data show noise at 10 dB SNR. 
Word level training and testing was done on one repetition of speech data 
from 64 talkers. Word prototypes were trained and tested on nonoverlapping 
gender-balanced sets of 32 talkers each. Under the clean training 
condition, word prototypes were built using the nominal clean phoneme 
models and 1 training pass over the noise-free training speech data. Word 
prototypes, for the multistyle training condition, used multistyle phoneme 
models and two training passes over the speech data: once clean and once 
with 10 dB SNR additive data show noise. Each recognition data point 
resulted from 3200 trials. 
Recognition rates are shown in FIG. 6 for the different stages and 
combinations in the system. The output of the hypothesizer (list of top 5 
word candidates) shows no critical deterioration (99.6% accuracy) even 
when compared to original fine match alone (99.3% accuracy for top 5 
candidates). Due to the independence of the errors made by the RC and TC 
stages, the word hypothesizer, which combines the scores from its two 
stages, achieves better top 1 recognition than any stage alone. The best 
top 1 recognition rate (96.5%) is achieved by the whole recognition 
system, where the fine match is run on the top 5 word candidates from the 
hypothesizer, and the final word decision is made by combining the 
normalized scores from the hypothesizer and the fine match. 
Top 1 recognition rates under two training speech conditions and 5 test 
speech conditions are shown in FIG. 7. The effect of multistyle training 
on error rate was not found to be significant (p=0.05, by McNemar test) in 
clean test conditions, and was found to significantly reduce the error 
rate by 22% to 66% in noisy test conditions. For more information on the 
McNemar test see "Some Statistical Issues in the Comparison of Speech 
Recognition Algorithms," by L. Gillick and S. J. Cox, Proc. ICASSP, 1989, 
pp. 532-535. Use of the word hypothesizer improved recognition performance 
(compared to exhaustive search by the fine match alone) for every test 
condition under multistyle training. The error reduction due to the 
hypothesizer was insignificant (2%) for 10 dB car noise, but was 25% or 
more for each of the 4 other test conditions. 
The measure time for the alignment portion of the matching (independent of 
fixed overhead for analysis and phoneme similarity computation) is shown 
in the left side of FIG. 8. The times reported here were for nonoptimized 
software. For a 100 word lexicon the whole system requires only 7.3% of 
the alignment time used by the fine match alone. For larger lexicons the 
alignment time reduction is yet larger, as shown in the right side of FIG. 
8. 
A summary of the recognition performance and resource requirements of the 
hypothesizer alone, and in combination with the MSM fine match procedure, 
is shown in Table I. On the 100 word name recognition task, use of the 
word hypothesizer decreased alignment time to 7.3% of the time required by 
the fine match, while increasing the memory size of the reference data by 
76%. Error rate was decreased significantly: by 30% or more (p&lt;0.001) for 
clean or data show noise-corrupted test speech at up to 10 dB 
signal-to-noise ratio. 
TABLE I 
______________________________________ 
Alignment 
Memory System Error Rate 
Time Ratio 
Size Clean 20 dB 10 dB 
______________________________________ 
Fine Match 
100% 600 5.8 7.2 11.2 
Hypothesizer 
2.3% 457 5.1 5.7 12.2 
Whole System 
7.3% 1057 4.0 4.4 7.9 
______________________________________ 
As suggested by Table I, the word hypothesizer may be useful by itself, as 
a low complexity speech recognizer. Alignment time, memory size and error 
rate under clean or mild noise conditions are in fact superior to the fine 
match procedures. The robustness of the word hypothesizer's top 1 
recognition performance under various other adverse conditions is under 
current investigation. 
The multistage word hypothesizer, combined with the MSM fine match 
procedure, achieves low complexity, speaker-independent, medium-size 
vocabulary word recognition, suitable for implementation in inexpensive, 
small hardware. The word hypothesizer produced large reductions of 
computational complexity. On a 100 word task, alignment complexity was 
reduced by 93%, with significant error rate reduction for clean and noisy 
test conditions.