Voice morphing
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Voice morphing means the transition of one speech signal into another. Like image morphing, speech morphing aims to preserve the shared characteristics of the starting and final signals, while generating a smooth transition between them. Speech morphing is analogous to image morphing. In image morphing the in-between images all show one face smoothly changing its shape and texture until it turns into the target face. It is this feature that a speech morph should possess. One speech signal should smoothly change into another, keeping the shared characteristics of the starting and ending signals but smoothly changing the other properties.

The major properties of concern as far as a speech signal is concerned are its pitch and envelope information. These two reside in a convolved form in a speech signal. Hence some efficient method for extracting each of these is necessary. We have adopted an uncomplicated approach namely cepstral analysis to do the same. Pitch and formant information in each signal is extracted using the cepstral approach. Necessary processing to obtain the morphed speech signal include methods like Cross fading of envelope information, Dynamic Time Warping to match the major signal features (pitch) and Signal Re-estimation to convert the morphed speech signal back into the acoustic waveform.


Speech morphing can be achieved by transforming the signal's representation from the acoustic waveform obtained by sampling of the analog signal, with which many people are familiar with, to another representation. To prepare the signal for the transformation, it is split into a number of 'frames' - sections of the waveform. The transformation is then applied to each frame of the signal. This provides another way of viewing the signal information. The new representation (said to be in the frequency domain) describes the average energy present at each frequency band.

Further analysis enables two pieces of information to be obtained: pitch information and the overall envelope of the sound. A key element in the morphing is the manipulation of the pitch information. If two signals with different pitches were simply cross-faded it is highly likely that two separate sounds will be heard. This occurs because the signal will have two distinct pitches causing the auditory system to perceive two different objects. A successful morph must exhibit a smoothly changing pitch throughout.

The pitch information of each sound is compared to provide the best match between the two signals' pitches. To do this match, the signals are stretched and compressed so that important sections of each signal match in time. The interpolation of the two sounds can then be performed which creates the intermediate sounds in the morph. The final stage is then to convert the frames back into a normal waveform

Voice morphing means the transition of one speech signal into
another. Like image morphing, speech morphing aims to preserve the
shared characteristics of the starting and final signals, while
generating a smooth transition between them. Speech morphing is
analogous to image morphing. In image morphing the in-between images
all show one face smoothly changing its shape and texture until it
turns into the target face. It is this feature that a speech morph
should possess. One speech signal should smoothly change into
another, keeping the shared characteristics of the starting and
ending signals but smoothly changing the other properties.
The major properties of concern as far as a speech signal is
concerned are its pitch and envelope information. These two reside in
a convolved form in a speech signal. Hence some efficient method for
extracting each of these is necessary. We have adopted an
uncomplicated approach namely cepstral analysis to do the same. Pitch
and formant information in each signal is extracted using the
cepstral approach. Necessary processing to obtain the morphed speech
signal include methods like Cross fading of envelope information,
Dynamic Time Warping to match the major signal features (pitch) and
Signal Re-estimation to convert the morphed speech signal back into
the acoustic waveform.
Voice morphing, which is also referred to as voice transformation and
voice conversion, is a technique for modifying a source speakerâ„¢s
speech to sound as if it was spoken by some designated target
speaker. There are many applications of voice morphing including
customizing voices for text to speech (TTS) systems, transforming
voice-overs in adverts and films to sound like that of a well-known
celebrity, and enhancing the speech of impaired speakers such as
laryngectomees. Two key requirements of many of these applications
are that firstly they should not rely on large amounts of parallel
training data where both speakers recite identical texts, and
secondly, the high audio quality of the source should be preserved in
the transformed speech. The core process in a voice morphing system
is the transformation of the spectral envelope of the source speaker
to match that of the target speaker and various approaches have been
proposed for doing this such as codebook mapping, formant mapping,
and linear transformations. Codebook mapping, however, typically
leads to discontinuities in the transformed speech. Although some
discontinuities can be resolved by some form of interpolation
technique , the conversion approach can still suffer from a lack of
robustness as well as degraded quality. On the other hand, formant
mapping is prone to formant tracking errors. Hence, transformation-
based approaches are now the most popular. In particular, the
continuous probabilistic transformation approach introduced by
Stylianou provides the baseline for modern systems. In this approach,
a Gaussian mixture model (GMM) is used to classify each incoming
speech frame, and a set of linear transformations weighted by the
continuous GMM probabilities are applied to give a smoothly varying
target output. The linear transformations are typically estimated
from time-aligned parallel training data using least mean squares.
More recently, Kain has proposed a variant of this method in which
the GMM classification is based on a joint density model. However,
like the original Stylianou approach, it still relies on parallel
training data. Although the requirement for parallel training data is
often acceptable, there are applications which require voice
transformation for nonparallel training data. Examples can be found
in the entertainment and media industries where recordings of unknown
speakers need to be transformed to sound like well-known
personalities. Further uses are envisaged in applications where the
provision of parallel data is impossible such as when the source and
target speaker speak different languages. Although interpolated
linear transforms are effective in transforming speaker identity, the
direct transformation of successive source speech frames to yield the
required target speech will result in a number artifacts. The reasons
for this are as follows. First, the reduced dimensionality of the
spectral vector used to represent the spectral envelope and the
averaging effect of the linear transformation result in formant
broadening and a loss of spectral detail. Second, unnatural phase
dispersion in the target speech can lead to audible artifacts and
this effect is aggravated when pitch and duration are modified.
Third, unvoiced sounds have very high variance and are typically not
transformed. However, in that case, residual voicing from the source
is carried over to the target speech resulting in a disconcerting
background whispering effect .To achieve high quality of voice
conversion, include a spectral refinement approach to compensate the
spectral distortion, a phase prediction method for natural phase
coupling and an unvoiced sounds transformation scheme. Each of these
techniques is assessed individually and the overall performance of
the complete solution evaluated using listening tests. Overall it is
found that the enhancements significantly improve.
Chapter 2
2.1 Overall Framework
Transform-based voice morphing technology converts the speaker
identity by modifying the parameters of an acoustic representation of
the speech signal. It normally includes two parts, the training
procedure and the transformation procedure. The training procedure
operates on examples of speech from the source and the target
speakers. The input speech examples are first analyzed to extract the
spectral parameters that represent the speaker identity. Usually
these parameters encode the short-term acoustic features, such as the
spectrum shape and the formant structure. After the feature
extraction, a conversion function is trained to capture the
relationship between the source parameters and the corresponding
target parameters. In the transformation procedure, the new spectral
parameters are obtained by applying the trained conversion functions
to the source parameters. Finally, the morphed speech is synthesized
from the converted parameters. There are three interdependent issues
that must be decided before building a voice morphing system. First,
a mathematical model must be chosen which allows the speech signal to
be manipulated and regenerated with minimum distortion. Previous
research suggests that the sinusoidal model is a good candidate
since, in principle at least, this model can support modifications to
both the prosody and the spectral characteristics of the source
signal without inducing significant artifacts However, in practice,
conversion quality is always compromised by phase incoherency in the
regenerated signal, and to minimize this problem, a pitch synchronous
sinusoidal model is used in our system .Second, the acoustic features
which enable humans to identify speakers must be extracted and coded.
These features should be independent of the message and the
environment so that whatever and wherever the source speaker speaks,
his/her voice characteristics can be successfully transformed to
sound like the target speaker. Clearly the changes applied to these
features must be capable of straightforward realization by the speech
model. Third, the type of conversion function and the method of
training and applying the conversion function must be decided
2.2 Spectral Parameters
As indicated above, the overall shape of the spectral envelope
provides an effective representation of the vocal tract
characteristics of the speaker and the formant structure of voiced
sounds. Generally, there are several ways to estimate the spectral
envelope,such as using linear predictive coding (LPC) , cepstral
coefficients, and line spectral frequencies (LSF). The main steps in
estimating the LSF envelope for each speech frame are as follows.
1. Use the amplitudes of the harmonicsdetermined by the pitch
synchronous sinusoidal model to represent the magnitude spectrum.K is
determined by the fundamental frequency , its value can typically
range from 50 to 200.
2. Resample the magnitude spectrum nonuniformly according to the
bark scale frequency warping using cubic spline interpolation.
3. Compute the LPC coefficients by applying the Levinson- Durbin
algorithm to the autocorrelation sequence of the warped power
4. Convert the LPC coefficients to LSF.
5. In order to maintain adequate encoding of the formant
structure,LSF spectral vectors with an order of p=15 were used
throughout our voice conversion experiments.
2.3 Linear Transforms
We now turn to the key problem of finding an appropriate conversion
function to transform the spectral parameters. Assume that the
training data contains two sets of spectral vectors X and Y which,
respectively, encode the speech of the source speaker and the target
speaker A straightforward method to convert the source vectors is to
use a linear transform. In the general case, the linear
transformation of a p dimensional vector x is represented by a p*
(p+1)dimensional matrix W applied to the extended vector x=[xâ„¢,1]â„¢.
Since there are a wide variety speech sounds, a single global
transform is not sufficient to capture the variability in human
speech. Therefore, a commonly used technique is to classify the
speech sounds into classes using a statistical classifier such as a
Gaussian mixture model (GMM) and then apply a class-specific
transform. However, in practice, the selection of a single N
transform from a finite set of transformations can lead to
discontinuities in the output signal. In addition, the selected
transform may not be appropriate for source vectors that fall in the
overlap area between classes. Hence, in order to generate more robust
transformations, a soft classification is preferred in which all N
transformations contribute to the conversion of the source vector.
The contribution degree of each transformation matrix depends on the
degree to which that source vector belongs to the corresponding
speech class.
1) Least Square Error Estimation: When parallel training data is
available, the transformation matrices can be estimated directly
using the least square error (LSE) criterion. In this case, the
source and target vectors are time aligned such that each source
training vector xi corresponds to a target training vector yi For
ease of manipulation, the general form of the interpolated
transformation in (2) can be rewritten compactly as
The accurate alignment of source and target vectors in the training
set is crucial for a robust estimation of the transformation
matrices. Normally, a dynamic time warping (DTW) algorithm is used to
obtain the required time alignment where the local cost function is
the spectral distance between source and target vectors. However, the
alignment obtained using this method will sometimes be distorted when
the source and target speakers are very different, this is especially
a problem in cross gender transformation.
2) Maximum Likelihood Estimation: As noted in the introduction, the
provision of parallel training data is not always feasible and hence
it would be useful if the required transformation matrices could be
estimated from nonparallel data. The form of suggests that, analogous
to the use of transforms for adaptation in speech recognition,
maximum likelihood (ML) should provide a framework for doing this. To
estimate multiple transforms using this scheme, a source GMM is used
to assign the source vectors to classes via as in the LSE estimation
scheme. A transform matrix is then estimated separately for each
class using the above ML scheme applied to just the data for that
class. Though it is theoretically possible to estimate multiple
transforms using soft classification, in practice, matrices will
become too large to invert. Hence, the simpler hard classification
approach is used here. As with the least mean squares method using
parallel data, performance is greatly improved if subphone segment
boundaries can be accurately determined in the source data using the
target HMM and forced alignment recognition mode. This enables the
set of Gaussians evaluated for each source frame to be limited to
just those associated with the HMM state corresponding to the
associated subphone. This does, of course, require that the
orthography of the source utterances be known. Similarly, knowing the
orthography of the target training data makes training the target HMM
simpler and more effective.
Chapter 3
The converted speech produced by the baseline system described above
will often contain artifacts. This section discusses these artifacts
in more detail and describes the solutions developed to mitigate
3.1 Phase Prediction
As is well known, the spectral magnitude and phase of human speech
are highly correlated. In the baseline system, when only spectral
magnitudes are modified and the original phase is preserved, a harsh
quality is introduced into the converted speech. However, to
simultaneously model the magnitude and phase and then convert them
both via a single unified transform is extremely difficult. A GMM
model is first trained to cluster the target spectral envelopes coded
via LSF coefficients into M classes (C1,¦¦.,CM). For each target
envelope v we have a set of posterior probabilities. This can be
regarded as another form of representation of the spectral shape. A
set of template signal T = [T1,¦¦., TM] can be estimated by
minimising the waveform shape prediction error.
3.2 Spectral Refinement
Although the formant structure of the source speech has been
transformed to match the target, the spectral detail has been lost as
a result of reducing the dimensionality of the envelope
representation during the transform. Another clearly visible effect
is the broadening of the spectral peaks caused, at least in part, by
the averaging effect of the estimation method. All these degradations
lead to muffled effects in the converted speech. To solve this
problem, a straightforward idea is to reintroduce the lost spectral
details to the converted envelopes. A spectral residual prediction
approach has been developed to do this based on the residual codebook
method, where the codebook is trained using a GMM model. After the
residual codebook is obtained, the spectral residual needed to
compensate each converted spectral envelope can be predicted
straightforwardly based on the posterior probabilities.
3.3 Transforming Unvoiced Sounds
Many unvoiced sounds, have some vocal tract coloring and simply
copying the source to the target affects the converted speech
characteristics, especially in cross gender conversion. A typical
effect is the perception of another speaker whispering behind the
target speaker. Since most unvoiced sounds have no obvious vocal
tract structure and cannot be regarded as short-term stationary
signals, their spectral envelopes show large variations. Therefore,
it is not effective to convert them using the same solution as for
voiced sounds. However randomly deleting, replicating and
concatenating segments of the same unvoiced sound does not induce
significant artifacts. This observation suggests a possible solution
based on unit selection and concatenation to transform unvoiced
Chapter 4
In real time voice morphing what we want is to be able to morph, in
real-time user singing a melody with the voice of another singer. It
results in an impersonating system with which the user can morph
his/her voice attributes, such as pitch, timbre, vibrato and
articulation, with the ones from a prerecorded target singer. The
user is able to control the degree of morphing, thus being able to
choose the level of impersonation that he/she wants to accomplish.
In our particular implementation we are using as the target voice a
recording of the complete song to be morphed. A more useful system
would use a database of excerpts of the target voice, thus choosing
the appropriate target segment at each particular time in the
morphing process. In order to incorporate to the userâ„¢s voice the
corresponding characteristics of the target voice, the system has
to first recognize what the user is singing(phonemes and notes),
finding the same sounds in the target voice (i.e. synchronizing the
sounds), then interpolate the selected voice attributes, and finally
generate the output morphed voice. All this has to be accomplished in
Fig 4.1 System block diagram
4.1 The Voice Morphing System
Figure shows the general block diagram of the voice impersonator
system. The underlying analysis/synthesis technique is SMS to which
many changes have been done to better adapt it to the singing voice
and to the real-time constrains of the application. Also a
recognition and alignment module was added for synchronizing the
userâ„¢s voice with the target voice before the morphing is done.
Before we can morph a particular song we have to supply information
about the song to be morphed and the song recording itself (Target
Information and Song Information). The system requires the phonetic
transcription of the lyrics, the melody as MIDI data, and the actual
recording to be used as the target audio data. Thus, a good
impersonator of the singer that originally sang the song has to be
recorded. This recording has to be analyzed with SMS, segmented into
morphing units, and each unit labeled with the appropriate note and
phonetic information of the song. This preparation stage is done
semi-automatically, using a non-real time application developed for
this task. The first module of the running system includes the
realtime analysis and the recognition/ alignment steps. Each analysis
frame, with the appropriate parameterization, is associated with the
phoneme of a specific moment of the song and thus with a target
frame. The recognition/alignment algorithm is based on
traditionalspeech recognition technology, that is, Hidden Markov
Models (HMM) that were adapted to the singing voice. Once a user
frame is matched with a target frame, we morph them interpolating
data from both frames and we synthesize the output sound. Only voiced
phonemes are morphed and the user has control over which and by how
much each parameter is interpolated. The frames belonging to unvoiced
phonemes are left untouched thus always having the userâ„¢s consonants.
4.2 Voice analysis/synthesis using SMS
The traditional SMS analysis output is a collection of frequency and
amplitude values that represent the partials of the sound (sinusoidal
component), and either filter coefficients with a gain value or
spectral magnitudes and phases representing the residual sound (non
sinusoidal component. Several modifications have been done to the
main SMS procedures to adapt them to the requirements of the
impersonator system. A major improvement to SMS has been the real-
time implementation of the whole analysis/synthesis process, with a
processing latency of less than 30 milliseconds and tuned to the
particular case of the singing voice. This has required many
optimizations in the analysis part, especially in the fundamental
frequency detection algorithm. These improvements were mainly done in
the pitch candidate's search process, in the peak selection process,
in the fundamental frequency tracking process, and in the
implementation of a voiced-unvoiced gate. Another important set of
improvements to SMS relate to the incorporation of a higher-level
analysis step that extracts the parameters that are most meaningful
to be morphed. Attributes that are important to be able to
interpolate between the userâ„¢s voice and the targetâ„¢s voice in a
karaoke application include spectral shape, fundamental frequency,
amplitude and residual signal. Others, such as pitch micro
variations, vibrato, spectral tilt, or harmonicity, are also relevant
for various steps in the morphing process or to perform other sound
transformation that are done in parallel to the morphing. For
example, transforming some of these attributes we can achieve voice
effects such as Tom Waits hoarseness.
4.3 Phonetic recognition/alignment
This part of the system is responsible for recognizing the phoneme
that is being uttered by the user and also its musical context so
that a similar segment can be chosen from the target information.
There is a huge amount of research in the field of speech
recognition. The recognition systems work reasonably well when tested
in the well-controlled environment of the laboratory. However,
phoneme recognition rates decay miserably when the conditions are
adverse. In our case, we need a speaker independent system capable of
working in a bar with a lot of noise, loud music being played and not
very-high quality microphones. Moreover the system deals with singing
voice, which has never been worked on and for which there are no
available databases. It has to work also with very low delay, we
cannot wait for a phoneme to be finished before we recognize it and
we have to assign a phoneme to each frame.
Fig 4.2 Recognition and matching of morphable units.
This would be a rather impossible/impractical problem if it was not
for the fact that we know the words beforehand, the lyrics of the
song. This reduces a big portion of the search problem: all the
possible paths are restricted to just one string of phonemes, with
several possible pronunciations. Then the problem reduces to locating
the phoneme in the lyrics and placing the start and end points. We
have incorporated a speech recognizer based on phoneme-base discrete
HMM's that handles musical information and that is able to work with
very low delay. The details of the recognition system can be found in
another paper of our group. The recognizer is also used in the
preparation of the target audio data, to fragment the recording into
morphable units (phonemes) and to label them with the phonetic
transcription and the musical context. This is done out of real-time
for a better performance.
4.4 Morphing
Depending on the phoneme the user is singing, a unit from the target
is selected. Each frame from the user is morphed with a different
frame from the target, advancing sequentially in time. Then the user
has the choice to interpolate the different parameters extracted at
the analysis stage, such as amplitude, fundamental frequency,
spectral shape, residual signal, etc. In general the amplitude will
not be interpolated, thus always using the amplitude from the user
and the unvoiced phonemes will also not be morphed, thus always using
the consonants from the user. This will give the user the feeling of
being in control. In most cases the durations of the user and target
phonemes to be morphed will be different. If a given userâ„¢s phoneme
is shorter than the one from the target the system will simply skip
the remaining part of the target phoneme and go directly to the
articulation portion. In the case when the user sings a longer
phoneme than the one present in the target data the system enters in
the loop mode. Each voiced phoneme of the target has a loop point.
frame, marked in the preprocessing, non-real time stage. The system
uses this frame to loop-synthesis in case the user sings beyond that
point in the phoneme. Once we reach this frame in the target, the
rest of the frames of the user will be interpolated with that same
frame until the user ends the phoneme. This process is shown in
Fig 4.3 Loop synthesis diagram.
The frame used as a loop frame requires a good spectral shape and, if
possible, a pitch very close to the note that corresponds to that
phoneme. Since we keep a constant spectral shape, we have to do
something to make the synthesis sound natural. The way we do it is by
using some natural templates obtained from the analysis of a longer
phoneme that are then used to generate more target frames to morph
with out of the loop frame. One feature that adds naturalness is
pitch variations of a steady state note sung by the same target.
These delta pitches are kept in a look up table whose first access is
random and then we just read consecutive values. We keep two tables,
one with variations of steady pitch and another one with vibrato to
generate target frames. Once all the chosen parameters have been
interpolated in a given frame they are added back to the basic SMS
frame of the user. The synthesis is done with the standard synthesis
procedures of SMS
1. Quality-enhanced Voice Morphing using Maximum Likelihood
Transformations Hui Ye and Steve Young
2. High quality Voice Morphing Hui Ye and Steve Young
3. wikipedia.org
1. Introduction
2. Transform-based voice morphing system
2.1 Overall Framework
2.2 Spectral Parameters
2.3 Linear Transforms
3. System Enhancement
3.1 Phase Prediction
3.2 Spectral Refinement
3.3 Tansforming Unvoiced Sounds
4. Realtime voice morphing
4.1 The Voice Morphing System
4.2 Voice analysis/synthesis using SMS
4.3 Phonetic recognition/alignment
4.4 Morphing
5 References
Chapter 1
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30-01-2010, 04:20 PM

please read this also topicideashow-to-voice-morphing-full-report for voice morphing full report
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