Alma Mater Studiorum University of Bologna, August 22-26 2006

On human capability and acoustic cues
for discriminating singing and speaking voices
Yasunori Ohishi

Masataka Goto

Katunobu Itou

Kazuya Takeda

Graduate School ofInformation Science,
Nagoya University
Nagoya, Aichi, Japanohishi@sp.m.is.nagoyau.ac.jp

National Institute ofAdvanced Industrial Science and Technology(AIST)
Tsukuba, Ibaraki, Japanm.goto@aist.go.jp

Faculty of Computer andInformation Sciences,
Hosei University
Koganei, Tokyo, Japan
itou@k.hosei.ac.jp

Graduate School ofInformation Science,
Nagoya University
Nagoya, Aichi, Japankazuya.takeda@nagoya-u.jp

ABSTRACT

Keywords

In this paper, acoustic cues and human capability for discriminating singing and speaking voices are discussed todevelop an automatic discrimination system for singing
and speaking voices. Based on the results of preliminary
subjective experiments, listeners discriminate between
singing and speaking voices with 70.0% accuracy for 200ms signals and 99.7% for one-second signals. Since evenshort stimuli of 200 ms can be correctly discriminated, notonly temporal characteristics but also short-time spectralfeatures can be cues for discrimination. To examine howlisteners distinguish between these two voices, we conducted subjective experiments with singing and speaking
voice stimuli whose voice quality and prosody were systematically distorted by using signal processing techniques.
The experimental results suggest that spectral and prosodic
cues complementarily contributed to perceptual judgments.
Furthermore, a software system that can automaticallydiscriminate between singing and speaking voices and such
performances is also reported.

Singing voice, Speaking voice, Perception, Discrimination,
Voice quality, Prosody

INTRODUCTION
Sounds from the human mouth include such acoustic
events as speaking, singing, laughing, coughing, whistling,
and lip noises. Humans communicate by creatively using
these acoustic events because they can instantaneously
discriminate between such sounds by perceiving thevarious features that characterize them. The purpose of ourresearch is to clarify how humans discriminate between
these voices.
Among such acoustic events, this paper focuses on the discrimination between singing and speaking voices. Whenhumans sing, the vocal style can vary from the speaking
voice to some degree. Furthermore, singing voice is a vocal
style to which various emotions are added based on asong's key and its lyrics; that is, vocal style represents various emotional voices in an abstract form. Therefore, revealing the characteristics that influence the perception of thesinging voice creates the possibility of applications that
discriminate between other vocal styles, such as irate orwhispery voices.
Many research results have reported the characteristics of
singing voices, whose typical characteristics include thefundamental frequency (F0, perceived as pitch) and intensity that vary widely; the spectral envelope of the singing
voice has additional resonance at a medium frequency
range known as the singing formant (Sundberg, 1974).
Although the singing formant is observed in the voices ofopera singers, it is not necessarily observed in amateurs.

In: M. Baroni, A. R. Addessi, R. Caterina, M. Costa (2006) Proceedingsof the 9th International Conference on Music Perception & Cognition(ICMPC9), Bologna/Italy, August 22-26 2006.©2006 The Society forMusic Perception & Cognition (SMPC) and European Society for the
Cognitive Sciences of Music (ESCOM). Copyright of the content of anindividual paper is held by the primary (first-named) author of that paper. All rights reserved. No paper from this proceedings may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information
retrieval systems, without permission in writing from the paper's primaryauthor. No other part of this proceedings may be reproduced or transmitted in any form or by any means, electronic or mechanical, including
photocopying, recording, or by any information retrieval system, without
permission in writing from SMPC and ESCOM.

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However, humans can discriminate a singing from a speaking voice in daily conversation even if these voices areproduced by an amateur.
Also, previous work related to the singing voice includes acontrol model of fundamental frequency trajectory (Saito etal., 2002; Saito et al., 2004), general characteristics (Kawahara and Katayose, 2001; Edmund Kim, 2003), acoustic
differences between trained and untrained singers' voices(Omori et al., 1996; Brown et al., 2000; Watts et al.,2006), the subjective evaluation of common singing skills(Nakano et al., 2006), and singing voice morphing between
expressions (Yonezawa et al., 2005). On the other hand,previous work related to the discrimination between singing and speaking voices includes a holomorphic model ofthe differences in glottal air flow (Rothenberg, 1981; Alku
et al., 1992; Alku et al., 1996) and the dynamic characteristics of F0 trajectory (Shin et al., 2001). Therefore, mostprevious work has focused on either the singing or the
speaking voice.
Table 1. Listening samples based on signal length in investigation of signal length necessary for discrimination.
Signal length
100, 150, 200, 250,
500, 750, 1,000 ms
1,250 ms
1,500, 2,000 ms
Total

Singing voice

Speaking voice

25 signals
20 signals
10 signals
215 signals

25 signals
20 signals
10 signals
215 signals

HUMAN PERFORMANCE OFDISCRIMINATING SINGING AND
SPEAKING VOICES
We investigated the human capability to discriminate between singing and speaking voices by conducting a subjective experiment. First, we introduce the voice database that
we used. Second, we show subjective experimental conditions and results.

Voice database
We used 7,500 sound samples excerpted from an original
voice database called the ``AIST Humming Database''(Goto and Nishimura, 2005) developed at the National Institute of Advanced Industrial Science and Technology(AIST). Those samples, each about 7.0 to 12.0 seconds
long, consist of 3,750 samples of singing voices and 3,750
samples of speaking voices recorded from 75 subjects (37males, 38 females). At an arbitrary tempo without musicalaccompaniment, each subject sang two excerpts from thechorus and the first verses of 25 songs in different genres

100
95
Totalperform ance
Speaking perform ance
Singing perform ance

Accuracy rate [%]

90
85
80
75

(50 sound samples) and read the lyrics of those excerpts(50 sound samples), resulting in a total of 100 samples per
subject. The songs were selected from a popular music
database,“RWC Music Database: Popular Music”(RWC-MDB-P-2001) (Goto et al., 2002), which is an
original database available to researchers around the world.

70
65
60
55
50
0

500

1000
1500
Signallength [m s]2000

Figure 1. Human discrimination performance between
singing and speaking voices as a function of signal length.

Investigation of signal length necessary fordiscrimination
We investigated the signal length necessary for human listeners to discriminate between singing and speaking voices
by conducting a subjective experiment. In the experiment,
we used 5,000 voice signals (2,500 singing and 2,500
speaking voices) recorded from 50 subjects (25 males, 25
females) randomly selected from the voice database, andcut them into 50,000 voice signals of 10 different lengths(from 100 to 2,000 ms). 10 subjects listened to 430 signals(215 singing and 215 speaking voices) randomly extracted
from those 50,000 voice signals (Table 1) and determined

None of these works has presented knowledge based on
subjective and objective evaluations of acoustic featuresthat influence discrimination between voices. The goal ofthis study is to characterize the nature of singing andspeaking voices based on subjective experiments and build
measures that automatically discriminate between them.

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whether the voice signal is singing, speaking, or impossible
to discriminate.

domly extracted from 15,000 voice signals by random
splicing and 200 signals (100 singing and 100 speaking
voices) randomly extracted from 5,000 voice signals by
low-pass filtering (Table 2), and determined whether thevoice signal is singing or speaking.

Figure 1 shows that approximately one second is enoughfor humans to discriminate between singing and speaking
voices. Even with a 200-ms signal, discrimination accuracy
is more than 70%. This suggests that not only temporalcharacteristics corresponding to rhythm and melody but
also such short-term features as spectral envelopes carrydiscriminative cues between singing and speaking voices.

Discrimination results by random splicing technique

To compare the importance of temporal and spectral cues
for discrimination, we conducted subjective experiments
using two sets of stimuli whose voice quality and prosody
were distorted by using signal processing techniques, as
shown in Figure 2.

Table 2. Listening samples in investigation of acoustic
cues necessary for discrimination.

75

Low-pass filtering technique
65
60

The first set of stimuli was generated by randomly splicing
the waveform, i.e., dividing a signal into small pieces andrandomly concatenating them. In the set of stimuli, the temporal structure of the signal is distorted whereas short-time
spectral features are maintained (Scherer, 1985; Friend etal., 1996).

Speaking voice

70
65
60

Singing voice

Filtering (m ale voice)99.6%

Random Splicing (m ale voice)92.6%

75

O riginal(m ale voice)100%80

Random Splicing (fem ale voice)94.0%

In the experiment, we used 5,000 voice signals (2,500 singing and 2,500 speaking voices) recorded from the 50 subjects (25 males, 25 females) used above, and obtained
15,000 voice signals (7,500 singing and 7,500 speaking
voices) by random splicing, which cut one-second signals
into small pieces of three types (125, 200, and 250 ms) and
5,000 voice signals (2,500 singing and 2,500 speaking
voices) generated by low-pass filtering. 10 subjects listened
to 200 signals (100 singing and 100 speaking voices) ran-85

Filtering (fem ale voice)98.2%

Accuracy rate [%]90

O riginal(fem ale voice)100%95

O riginal(fem ale voice)100%100

The second set of stimuli was generated by low-pass filtering, i.e., eliminating frequency component higher than 800
Hz. This set of stimuli maintains the temporal structure of
the original signal although short-time spectral features are
distorted (Scherer, 1985).

ISBN 88-7395-155-4 © 2006 ICMPCSinging voice

Figure 3. Accuracy rate of one-second signals byrandom splicing and low-pass filtering techniques.

Random Splicing(m ale voice)74.0%

100 signals

Random Splicing(fem ale voice)80.5%

100 signals

70

Filtering (m ale voice)83.8%

Speaking voice

O riginal(m ale voice)98.6%

Total

Singing voice

Random Splicing(125 m s)70.6%

80

Random Splicing(200 m s)76.9%

85

Random Splicing(250 m s)84.3%

Filtering 86.9%

100 signals

90

Filtering (fem ale voice)90%

100 signals

Accuracy rate [%]

Total

95

O riginalvoice
99.3%

Random Splicing technique
Length of pieces
Singing voice
Speaking voice
125 ms40 signals
40 signals
200 ms40 signals
40 signals
250 ms20 signals
20 signals

O riginalvoice
100%

100

Filtering 98.9%

Investigation of acoustic cues necessary fordiscrimination

Random Splicing(250 m s)94.9%
Random Splicing(200 m s)90.0%
Random Splicing(125 m s)95.0%

In Figure 3, the discrimination results of singing and speaking voices are shown for one-second signals that were notdistorted at all. They are 99.3% and 100%, respectively.
However, the accuracy rate declines by random splicing.
The accuracy rate of singing voices especially declines as
the length of the pieces shortens from 250 to 125 ms.
When the length of the pieces is 125 ms, the accuracy rateof the singing voice is 70.6%, which is 28.7% lower than
the results of original voices. On the other hand, when thelength of the pieces is 125 ms, the accuracy rate of speaking voices is 95.0%, which is only 5.0% lower than the
results of original voices. We obtained the following comments from listeners after this experiment:

Speaking voice

Figure 4. Accuracy rate of one-second signals byrandom splicing and low-pass filtering techniques as afunction of vocal person’s gender.
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Discussion

When I listened to prolonged vowel production, I
judged it to be a singing voice.
I focused on the difference in voice quality between
singing and speaking voices.
When the amplitude fluctuation degree of a voice signal was great, I thought it was a singing voice.

•If the pitch varied widely, I thought it was a singing
voice.

•

It was easier to discriminate between singing andspeaking voices in female voices than in male voices
because the difference in pitch between them is wider
for female voices.

Because the temporal structure of the original singing
voices that correspond to rhythm and melody has been distorted to render them unavailable for discrimination, theaccuracy rate of singing voices by random splicing technique declined. It is also considered that listeners confusedsinging voices with speaking voices because of the shortvowels of singing voices divided by the random splicing
technique. Based on the investigation of vowel length for acertain signal that confused singing with speaking voices,
the vowel length of the original singing voice averaged
146.7 ms, and vowel length by random splicing averaged
73.3 ms: that is, half the vowel length of the original singing voice. On the other hand, in a signal that contained thesame lyrics read by the same subject, vowel length by random splicing averaged 60.0 ms. This only slightly changed
compared to the original average vowel length of 70 ms.
Vowel length is clearly an important cue for discrimination.
Consequently, the results clarified that a speaking voicegenerated by random splicing resembles a speaking voice;on the other hand, a singing voice generated by random
splicing also resembles a speaking voice.

Discrimination results by low-pass filtering
technique
In Figure 3, the discrimination results of singing and speaking voices by low-pass filtering are 86.9% and 98.9%, respectively. As in the random splicing technique, the accuracy rate of singing voice declines more than the speaking
voice.
Table 3. Analysis conditions of voice signals.
16 kHzHamming
25 ms
10 ms
24

6000
F0 [cent]

Sampling rate
Window
Frame length
Frame time
Mel-filterbank

5500
Singing voice

We obtained the following comments from listeners after
this experiment:

5000
0

•

5000

•

F0 [cent]

By focusing on differences in tempo, rate of speech,rhythm, and pitch fluctuation, I could discriminate between singing and speaking voices. When the amplitude fluctuation degree of a voice signal was great, I
thought it was a singing voice.

1.0

1.5
Tim e [s]

2.0

2.5

3.0

Speaking voice
4500

4000
0

If a voice signal contained a constant location in pitch,
I thought it was a singing voice.

0.5

0.5

1.0

1.5
Tim e [s]

2.0

2.5

3.0

Figure 5. F0 contour of singing and speaking voices
corresponding to identical lyrics.

Discrimination results for vocal person’s gender
Figure 4 shows discrimination results by random splicing
and low-pass filtering techniques for vocal person's gender.
The accuracy rate of female singing voices by random
splicing is 80.5%, which represents a mean accuracy rate of
125, 200 and 250 ms by length of the pieces. On the other
hand, the accuracy rate of male singing voices is 74.0%,
which is a decrease of 6.5% compared to female singing
voices. The accuracy rate of male singing voices by lowpass filtering is 83.8%, a decrease of 6.2% compared tofemale singing voices. These results show that discrimination between male singing and speaking voices is harderthan between female singing and speaking voices.

Despite eliminating frequency component higher than 800
Hz, a speaking voice can be distinguished from a singing
voice by perceiving the remaining prosody and tempo.
However, a singing voice by low-pass filtering is not always easy to distinguish from a speaking voice because thedistinction requires short-time spectral features. Although
the cut-off frequency of the filter is 800 Hz in this experiment, by varying this value, which frequency bands areimportant for discrimination remains a matter of future
research.

DISCRIMINATION MEASURES
From subjective experiments, human listeners distinguished between singing and speaking voices with 100%
accuracy for one-second signals. On the other hand, even if

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the signal length was as short-term as 200 ms, the discrimination rate was 70.0%. Moreover, it was found that not
only temporal characteristics but also short-term spectralfeatures are important for discrimination.

the mixture as if it contains all possible harmonic structureswith different weights, which are calculated by Maximum
A Posteriori Probability (MAP) estimation.
Using the method, we determined the F0 value for every 10
ms, and then a F0 trajectory was smoothed by a medianfilter of a 100-ms moving window. Furthermore, ∆F0 is
calculated by five-point regression, as in the MFCC case.

Therefore, to objectively clarify how these features contribute to the discrimination of the two styles, we propose
an automatic vocal style discriminator that can discriminate
between singing and speaking voices by using two different measures: short-term and long-term feature measures.
The short-term feature measure exploits the spectral envelope represented by using Mel-Frequency Cepstrum Coefficients (MFCC) and their derivatives (∆MFCC). The longterm feature measure exploits the dynamics of F0 extracted
from voice signals.

Training the discriminative model
In this approach, the distribution of MFCC vectors or ∆F0values are represented by 16-mixture Gaussian Mixture
Models (GMM) trained on the training set using the expectation maximization algorithm for both singing and speaking voice signals. The variances of distributions were modeled by a diagonal covariance matrix. Discrimination was
performed through the maximum likelihood principle:

Short-term spectral feature measure
To measure a spectral envelope, Mel-Frequency Cepstrum
Coefficients (MFCC) and their derivatives (∆MFCC),
which are successfully used for envelope extraction in
speech recognition applications, were used. As shown in
Table 3, every 10 ms, MFCC are calculated for 25-ms
hamming windowed frames; ∆MFCC is calculated as regression parameters over five frames.

dˆ =where x n is the nth feature vector, N is input signal lengthand Λ d ( d = singing, speaking) are the GMM parameters for the distribution of MFCC vectors. Functionf calculates posterior probability by using all GMM parameters
for both singing and speaking voices.

2000

Long-term feature measure
Since the singing voice is generated under the constraintsof melodic and rhythm patterns, the dynamics of prosody
differ from the speaking voice. Therefore, the dynamics ofprosody extracted from voice signals are expected to becues for automatically discriminating between singing andspeaking voices (Figure 5).
F0 is estimated by using the predominant-F0 estimation
method of Goto et al. (Goto et al., 1999) that estimates therelative dominance of every possible harmonic structure in
the sound mixture and determines the F0 of the most predominant one. Relative dominance is obtained by treating

100
80
60

95.0%
81.7%

90.0%
79.3%

70.6%

Discrim ination oflong signals
Listening one-second originalsignals
Low-pass filtering
Autom atic discrim ination
ofone-second signals (∆F0)

Singing voice

82.2%

1500

Figure 6. Comparing and integrating two measuresusing a spectral envelope (MFCC) and ∆F0.

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76.9%

40

98.9%

1000
Signallength [m s]

51.4%

50

Total performanceof ∆F0 GMM
500

60

86.9%

40
0

70

99.3%

50

Total performanceof MFCC+∆MFCC GMM

80

Listening 200-m s originalsignals
Random Splicing (200 m s)
Random Splicing (125 m s)
200-m s signals'autom atic
discrim ination (M FCC+∆M FCC )60.7%

Accuracy rate [%]Total performanceof MFCC+∆MFCC+∆F0 GMM

Accuracy rate [%]Accuracy rate [%]

90

80

60

Discrim ination of short signals

100

100%

Human performance
90

76.8%

100

70

1 N
∑ log f (x n ; Λ d ), @(1)d =singing, speaking N n =1
arg max

Speaking voice

Figure 7. Comparing automatic discrimination performancewith results of subjective experiments.

EVALUATION OF PROPOSED METHOD
In this section, we show experimental evaluations forautomatic discrimination between singing and speaking
voices. In evaluating the discrimination performance using
the spectral envelope and the dynamics of F0, 7,500 soundsamples of singing and speaking voices from 75 subjects
were used to train the GMMs of the feature vectors and totest the method. A fifteen-fold cross-validation approach
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Even though short-term spectral features are distorted byeliminating frequency component higher than 800 Hz, humans can distinguish between those voices by perceivingsuch temporal features of signals as melody and rhythm
patterns. In other words, the temporal features included inlong-term signals are important for discriminating between
those voices. When comparing the automatic discrimination results using ∆F0 with the subjective experimental
results, the discrimination results are low, as shown in Figure 7. In this paper, ∆F0 is calculated as regression parameters over five frames (50 ms) of F0 that are estimated continuously. However, from the subjective experimental results, humans distinguish between those voices by perceiving continuous changes of F0 longer than 50 ms. Therefore, a longer ∆F0 calculation method that considers the F0interpolation of unvoiced sounds is needed to further improve the performance.

was used for evaluation. First, sound samples from 75 subjects were divided into fifteen groups. Eight of the fifteengroups were used for GMM training, and the rest wereused as a test. An average discrimination rate was obtained
from the fifteen cross-validation tests.
In Figure 6, discrimination results using MFCC+∆MFCC
and ∆F0 are plotted. MFCC was used up to the 12th coefficients. In both measures absolute performance improved
when a longer signal was available. For input signals
shorter than one second, MFCC performed better, whereas∆F0 performed better for signals longer than one second.
Finally, two measures were integrated into a 25dimensional vector. It can be seen from Figure 6 that discrimination performance is improved by 2.6% for twosecond signals.

DISCUSSION
The results clarified that the two measures can effectively
capture the signal features that discriminate between singing and speaking voices. Discrimination using MFCC and∆MFCC is effective for less than one-second signals. The
difference between the spectrum envelopes of singing andspeaking voices is a dominant cue for the discrimination ofshort signals. On the other hand, discrimination using ∆F0
is effective for signals of one second or longer. The GMM
of ∆F0 appropriately deals with the differences of the
global F0 contours of singing and speaking voices by modeling the local changes of F0.

CONCLUSION
In this paper, we discussed acoustic cues and human capability for discriminating singing and speaking voices.
When investigating the signal length necessary for singing
and speaking voice discrimination, we showed that humans
can discriminate singing and speaking voices 200-ms longand one-second long with 70.0% and 99.7% accuracy,
respectively. By conducting subjective experiments withvoice signals whose voice quality and prosody were systematically distorted by signal processing techniques, we
showed that spectral and prosodic cues complementarily
contributed to perceptual judgments.
Furthermore, by hypothesizing that listeners depend on
different cues based on the length of signals, we proposed
an automatic vocal style discriminator that can distinguishbetween singing and speaking voices by using two measures: spectral envelope (MFCC) and F0 derivative. In our
experimental results, when voice signals longer than one
second are discriminated, the F0-based measure outperforms the MFCC-based measure. On the other hand, when
voice signals shorter than one second are discriminated, the
MFCC-based measure outperforms the F0-based measure.
While discrimination accuracy with the F0-based measureis 85.0% for two-second signals, a simple combination of
two measures improves it by 2.3% for two-second signals.
However, compared with human capability, discrimination
performance is low, especially when the test signal isshorter than one second. In the future, we plan to clarify thedifferences of spectral features between singing and speaking voices and to discuss a longer F0 contour modelingmethod.

Furthermore, we compared automatic discrimination performances with the results of subjective experiments. Whenthe temporal structure of the signal is distorted by a random
splicing technique, human capability for discriminating
between singing and speaking voices decreased because thevowel length is shorter than the original singing voice signals. However, when the length of the pieces in the random
splicing technique is 125 ms, human capability is 70.6%.
Based on this result, the short-term spectral features of signals affect discrimination. When comparing the automatic
discrimination results of 200-ms singing voices using
MFCC and ∆MFCC with the human capability of 200-ms
singing voices, although the automatic discrimination result
decreased by 9.3% compared with human capability, thishuman capability is more similar to the automatic discrimination result using MFCC and ∆MFCC than any otherautomatic discrimination results in the above chart of Figure 7. Consequently, this result shows the importance ofspectral features for automatically discriminating between
singing and speaking voices. MFCC is successfully used torepresent the phoneme structure in speech recognition applications; however, to discriminate between those voices,
we need to focus on the features which can not be represented by MFCC. In the future, we plan to propose newmeasures to improve the automatic discrimination performance.

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