Effect of Dynamic Time Warping on Alignment of Phrases and Phonemes

International Journal on Natural Language Computing (IJNLC) Vol. 1, No.3, October 2012
DOI : 10.5121/ijnlc.2012.1302 9
EFFECT OF DYNAMIC TIME WARPING ON
ALIGNMENT OF PHRASES AND PHONEMES
Padmini Rajput, Radhika Khanna, Parveen Lehana and Jang Bahadur Singh
Department of Physics and Electronics, University of Jammu, Jammu, India
*pklehanajournals@gmail.com
ABSTRACT
Speech synthesis and recognition are the basic techniques used for man-machine communication. This type
of communication is valuable when our hands and eyes are busy in some other task such as driving a
vehicle, performing surgery, or firing weapons at the enemy. Dynamic time warping (DTW) is mostly used
for aligning two given multidimensional sequences. It finds an optimal match between the given sequences.
The distance between the aligned sequences should be relatively lesser as compared to unaligned
sequences. The improvement in the alignment may be estimated from the corresponding distances. This
technique has applications in speech recognition, speech synthesis, and speaker transformation. The
objective of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The speech signals in the form of twenty five
phrases were recorded from each of six speakers (3 males and 3 females). The recorded material was
segmented manually and aligned at sentence and phoneme level. The aligned sentences of different speaker
pairs were analyzed using HNM and the HNM parameters were further aligned at frame level using DTW.
Mahalanobis distances were computed for each pair of sentences. The investigations have shown more than
20 % reduction in the average Mahalanobis distances.
KEYWORDS
Speech recognition, Dynamic time warping DTW, Mahanalobis distance, segmentation & alignment.
1. INTRODUCTION
Speech signal is generated as a consequence of exciting a dynamic vocal tract system with time
varying excitation. Speech is the most innate and fastest means of human interaction. The
attributes of speech have made researchers to continuously improve the man-machine
communication leading to the development of efficient and intelligent techniques like speech
recognition, which is continuously gaining a serious attention since more than fifty years [1] [2].
This type of communication is valuable when our hands and eyes are busy in some other task
such as driving a vehicle, performing surgery, or firing weapons at the enemy. Speech recognition
also known as automatic speech recognition (ASR) converts spoken language in text. It has been
two decades since the ASR have started moving from research labs to real-world. Speech
recognition is more difficult than speech generation, in spite of the fact that computers can store
and recall enormous amounts of data, perform mathematical computations at very high speed, and
do repetitive tasks without losing any type of efficiency. The reason for this may be attributed to
the lack of general knowledge in the computers. Because of these limitations, the accuracy of
speech recognition is reduced. There are two main steps for speech recognition: feature extraction
and feature matching. Each word in the input speech signal is isolated and then analyzed to

10
obtain the parameters such as Mel frequency cepstral coefficients (MFCCs) or line spectral
frequencies (LSF). These parameters provide the information related to the dynamically changing
vocal tract during speech production. These parameters are then compared with previous
examples of spoken words to identify the closest match. Similar steps may be used for identity
matching of a given speaker [3].
Modern architectures for Automatic Speech Recognition (ASR) are mostly software architectures
that construct a sequence of word hypotheses out of an acoustic signal. In recent years the use of
Multi-layer perceptron (MLP) derived acoustic feature has gradually become more popular in
ASR systems [4]. There are two types of speech recognition: speaker-dependent and speaker-
independent. Speaker-dependent technique works by learning the distinctiveness of a single
speaker like in case of voice recognition, while speaker-independent systems involves no training
as they are designed to recognize anyone's voice. As the acoustic spaces of the speakers are
multidimensional, reduction of their dimensionality is very important [5]. The most common
method for training of the speaker recognition system is hidden Markov model (HMM) and its
latest variant is (HMM-GMM) [6]. For better alignment or matching, normalization of the sub-
band temporal modulation envelopes may be used [7]. Although, a lot of effort has been put for
improving the speech recognition, until now the performances of such systems are unappealing in
real world tasks [8]. The main factors responsible for the stagnation in the fields of speech
recognition are environmental noise, channel distortion, and speaker variability [7] [9] [10]. For
alignment, segmentation is used and it is a technique by means of which the boundaries between
words, syllables, or phonemes in spoken languages are identified [1]. The segmentation is divided
into two levels. The lowest level of speech segmentation is carried out by the subdivision of a
sound into a sequence of phones. Another process makes use of lexical segmentation which
means the splitting up of sound into words of a language
.
The manual procedure involves an approach of listening and visual judgment in order to identify
the boundaries of meaningful speech segments. This process seems impractical in case of huge
data bases so different techniques are developed for this principle which led to the improvement
of automatic speech segmentation done by a chosen procedure or algorithms with the objective of
using the results for speech synthesis, data training for speech recognizers or to build and label
prosodic data basis [11]. The improvement in the alignment may be estimated from the
corresponding distances between the frames of the given sentences of two speakers. The objective
of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The aligned sentences of different
speaker pairs are analyzed using HNM and the HNM parameters were further aligned at frame
level using DTW. The justification for choosing HNM is that it is a very efficient model for
speech generation. The alignment is carried out only with voiced segments. For this, the harmonic
magnitudes are converted to LSF. Section 2 describes the working principle of HNM and its
parameter extraction procedure while Section 3 explains the methodology including dynamic time
warping. The results are presented in the Section 4.
2. HNM
HNM, a variant of sinusoidal model, is an analysis/modification/synthesis model which provides
high quality speech with less number of parameters, and with pitch and time scaling relatively
easy compared to all existing models and seems to be more promising for speech synthesis
compared to other existing models [11] [12]. Research has shown that all vowels and syllables
can be produced with a better quality syllables by the implementation of HNM [13]. Results
obtained from many speech signals including both male and female voices are quite satisfactory
with respect to the background noise and inaccuracies in the pitch [14]. In HNM, each segment of
speech can be modeled as two bands: “a lower harmonic part” can be represented using the

11
amplitudes and phases of the harmonics of a fundamental and an “upper “noise" part using an all
pole filter excited by random white noise, with dynamically varying band boundary.
Out of the two sub bands of the speech spectrum, one is modelled with harmonics of the
fundamental and the other is simulated using random noise. The harmonic part and noise part
constitute the quasi-periodic components and non-periodic part respectively [11]. The frequency
that separates the two bands is called maximum voiced frequency m
F . The lower band represents
the signal by harmonic sine waves, slowly varying in amplitudes and frequencies:
(1)
where ( )
l
a t and ( )
l t
 represent the amplitude and phase at time t of the th
l harmonic,
while ( )
o
w l t are the fundamental and time-varying number of harmonics included in the
harmonic part. AR model represents the upper band constituting the noise part modulated by the
time domain amplitude envelope. The noise part '( )
n t is obtained by filtering a white Gaussian
noise ( )
b t by a time varying, normalized all-pole filter ( : )
h t
 . The result obtained is multiplied
by an energy envelope function ( )
w t :
'( ) ( )[ ( ; )* ( )]
n t w t h t b t

= (2)
In addition to obtaining the maximum voiced frequency m
F , other parameters like
voiced/unvoiced, amplitudes and phase of harmonics of fundamental frequency (pitch), glottal
closure instants, parameters of noise part, and pitch are calculated for each frame. Figure 1
depicts the analysis using HNM. Speech signal is fed by the voicing detector which states the
frame either voiced or unvoiced. HNM analysis is pitch synchronous so their lies the exact
inference of the glottal closure instances (GCIs) [11]. GCIs can be calculated either by means of
the speech signal or electroglottogram (EGG). Speech signal or EGG is given at the input side of
GCI. Maximum voiced frequency m
F is calculated for each voiced frame. The analysis frame is
taken twice the local pitch period. Form each GCI the voiced part is analyzed for calculating
amplitudes and phase of all the pitch harmonics up to m
F .
The synthesized portion of the voice part is calculated from equation (1) for obtaining noise
parameters while the remaining fraction obtained as result of the subtraction of the noise from the
speech signal is the voiced part. Noise part is later analyzed for the LPC coefficients and energy
envelope. For both voiced and unvoiced frames the length of the analysis window for noise part is
taken as two local pitch periods. However for unvoiced frames the local pitch is the pitch of the
last frame and for voiced frames the local pitch is the pitch of the frame itself and [11]. The
addition of the synthesized speech.HNM based synthesis can be used for good quality output with
relatively small number of parameters. Using HNM, pitch and time scaling are also possible
without explicit estimation of vocal tract parameters [11]. Speaker transformation and voice
conversion method has been a hot area of research in speech processing research for the last two
decades [15] [16] [17] [18]. These techniques are also implemented in the framework of the
HNM system, which allows the high-quality modifications of speech signals. In comparison to
earlier methods based on the vector quantization, HNM based conversion scheme results in high
quality modification of speech signal [17].

12
3. METHODOLOGY
For the analysis of the speech signal, we have carried out the recording of six speakers in Hindi
language (3 males and 3 females). Speakers of different age group, from different regions of
Jammu have been taken. Twenty five sentences for each speaker were recorded. The recorded
material was segmented and labelled manually.
Figure 2. Estimation of Mahalanobis distances among
HNM parameters
Different sentences for different speaker combinations (male-female, female-female, and male-
male) were given as input to the HNM analysis module in order to obtain the HNM parameters of
the recorded speech (Figure 2). The HNM parameters are converted to LSFs and applied to the
next block for DTW [19]. DTW is an algorithm for measuring similarity between two sequences
which may vary in time or speed. DTW finds an optimal match between two given sequences.
Dynamic time warping is pattern matching based approach for finding an optimal distance
between two given sequences wrapped in a non-linear fashion under certain restrictions; it is a
well established technique for time alignment and comparison of speech and image patterns [20].
It is a form of dynamic programming and is extensively applied for pattern matching. We have
used two techniques for alignment. In the first technique, the unlabeled sentences were directly
given to the HNM. In second technique, phoneme marked sentences were given to the HNM. The
Mahalanobis distance (MD) was calculated between the aligned frames. The Mahalanobis
distance in parametric space was estimated using the following relation defined for feature
vectors X and Y as
( ) ( )
T 1
M ( , )
D −
=
X Y X - Y X - Y
 . (3)
where Σis the covariance matrix of the feature vectors used in training [21].
4. RESULTS
The average Mahalanobis distances calculated for each speaker pair is listed in Table 1. It is clear
from this table that more than 20 % improvement in alignment is obtained if the phoneme marked
sentences are given as input to the HNM and consequently to DTW. It may be noted that silence
frames and unvoiced segments were removed before applying the frames to the DTW. Our
preliminary investigations have shown that DTW does not perform satisfactory for these frames.
The alignment time sequence (output of the DTW) and the Mahalanobis distances for three
speaker pairs (female to female, female to male, and male to male) are shown in Figure 3.
Although, the curves for Mahalanobis distances for the unlabeled sentences and the phoneme
marked sentences are very near to each other, the percentage improvement is observed more for
phoneme marked sentences.

13
Table 1. Average Mahalanobis distances.
Figure 3. Plots for time relationship between DTW aligned frames (first column) and
variation of Mahalanobis distances with respect to time (second column).
5. CONCLUSIONS
Investigations were carried out with three different combinations of male-male, male-female and
female-female speakers for estimating the average Mahalanobis distances for unlabeled and
phoneme marked sentences. From the above results we observe that phoneme based alignment is
always far better than phrase level alignment. The results were quite obvious but the percentage
of improvement (more than 20 %) constraints the necessity of using phoneme based alignment for
developing efficient mapping for speaker recognition and speaker transformation.
Speaker Pair Sentence based
distance
Phoneme based
distance
Improvement
(%)
F2F 5.78 4.50 22
F2M 6.00 4.73 21
M2M 9.10 7.02 23

14
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