Audio Engineering Society. Convention Paper. Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA

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1 Audio Engineering Society Convention Paper Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA The papers at this Convention have been selected on the basis of a submitted abstract and extended précis that have been peer reviewed by at least two qualified anonymous reviewers. This convention paper has been reproduced from the author's advance manuscript, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for the contents. Additional papers may be obtained by sending request and remittance to Audio Engineering Society, 60 East 42 nd Street, New York, New York , USA; also see All rights reserved. Reproduction of this paper, or any portion thereof, is not permitted without direct permission from the Journal of the Audio Engineering Society. VOICE SAMPLES RECORDING AND SPEECH QUALITY ASSESSMENT FOR FORENSIC AND AUTOMATIC SPEAKER IDENTIFICATION Andrey Barinov 1 1 Speech Technology Center Ltd., Saint Petersburg, Russia ABSTRACT The task of speaker recognition or speaker becomes very important in our digital world. Most of the law enforcement organizations use either automatic or manual speaker tools for investigation processes. In any case, before carrying out the analysis, they usually need to record a voice sample from the suspect either for one to one comparison or to fill in the database. In this paper we describe the parameters of speech signal which are important for speaker performance, we propose the approaches of quality assessment and provide the practical recommendations of taking the high quality voice sample, acceptable for speaker. The materials of this paper might be useful for both soft/hardware developers and forensic practitioners. 1. INTRODUCTION Nowadays, when mobile means of communication are very popular and developing rapidly, the forensic acoustics and especially speaker procedure become very important part of law enforcement activity [1, 2]. Typically, speaker examination requires two audio recordings: a questionable recording and a voice sample. The questionable recording is in most of the cases the intercepted or recorded phone call, but the sample is usually taken from the suspect in the police department. Further these recordings go either to the expert or to the automatic system to accomplish the speaker investigation. There are a lot of papers [3-5] describing the problems with questionable/unknown recording and the ways to solve some of them, or at least to partially compensate for their influence. That is why in this paper we do not consider this part of the problem. This paper has to do with the procedures of voice sample recording and speech signal quality assessment. In the second part of the paper we discuss about the parameters of audio signal which can affect the speech analysis (overloading, reverberation, compression etc.) and

2 might make the procedure very difficult and the results very doubtful. The third part of the paper describes the values of these parameters, having which; the recording would be fully acceptable for speaker. These values were obtained during a series of experiments with both experts and automatic systems. The fourth part of the paper proposes the approaches, equipment and recommendations of taking the high quality voice sample, which has all the parameters corresponding to the requirements imposed in the third part. It also describes some methods and tools for speech quality estimation in the context of speaker. In the conclusions we give the summary of the accomplished work and describe the directions of future researches Overloading Overloading or clipping occurs when the dynamic range of one of the elements in the recording chain does not match to the dynamic range of the recorded sound signal. It is a sort of signal s amplitude limitation. In the simple waveform this effect looks as shown in the Fig SPEECH SIGNAL S PARAMETERS Speech signal has a lot of different parameters. We can roughly split them into three groups. The first group refers to quantity. For example, duration of the voice sample belongs to this group. Second group refers to quality. Signal to Noise ratio, clipping, frequency range etc. belong to this group. And the third group refers to comparability. Speakers should be in the same emotional state etc. This paper has to do only with the second group of parameters, which is quality of the voice sample. A lot of different approaches to speaker have been developed since the first voice examination. All of them, except those which are based on listening (linguistic, auditory, psychological etc.) has to do with spectral analysis (voiceprint, microanalysis, formant matching etc.). Hence, the quality and accuracy of spectral picture is the most important factor for both experts and automatic systems [6-8]. This is why in this paper we describe only those parameters which affect instrumental analysis and we leave behind this paper all the parameters which can affect subjective listening and speech perception. Saying this we keep in mind that each of these parameters, affecting spectrum, affects also the perceived quality of speech. As the result of said above, our list of parameters for further consideration looks as follows: Overloading; Signal-to-noise ratio; Reverberation; Nonlinearity of frequency response; Sampling frequency and bit rate. Figure 1: Clipped speech signal. For better understanding how clipping can affect the spectrum of the signal let s consider the single sine wave. Figure 2 shows the initial 100 Hz sine wave and the same one after clipping. Figure 2: Original 100 Hz sine wave and after clipping. The average spectra of these two signals are presented in Fig. 3. Thus, clipping changes the spectrum of each sine wave in the speech signal. The additional frequencies appear. It means that all kinds of spectral based speaker analyses will be affected with clipping. Page 2 of 10

3 how much higher the level of useful signal is than the level of noises (unwanted signal). High quality speech signal looks as in the Fig.6 and has an SNR around db as it is shown in Fig.7. Figure 3: Average spectra of the original 100 Hz sine wave and after clipping. Figure 6: High quality speech signal without any noises. Figure 4: Dynamic LPC spectrograms of initial o -like sound and after clipping. Figure 7: SNR of high quality speech signal is around db. Figure 5: Instantaneous LPC spectra of initial o -like sound and after clipping. Figures 4-5 show an example of speech signal clipping for one single sound. It can be seen that level of additional frequencies in the range Hz is comparable with the level of original third formant (2900 Hz). It might cause the false acceptance of these frequencies as a formant by both automatic feature extraction algorithms and experts Signal-to-noise ratio Signal-to-noise ratio (SNR) is a parameter which determines the overall signal s quality and describes Figure 8: Noisy recording. Noisy signal can contain a mixture of different types of noises which mask the useful signal as a perceived audio and mask the features in a form of visible speech. The simple waveform, SNR and Page 3 of 10

4 dynamic LPC spectrogram of a noisy signal are shown in Fig respectively. Thus, it becomes sometimes impossible to extract individual features (almost all of them are spectral based) of the speaker from a noisy speech sample without noise cancellation processing. The minimum SNR when the speech is still intelligible is around 3-6 db. signal is a combination of the initial signal and its reflections which come to the microphone with the time delays, which depend on the distance to the reflecting surfaces. The more solid is the reflecting surface, the more similar to the initial signal the reflected one will be. It s just very rough description of the effect, but it is enough to understand that reverberation changes the sounding, waveform and the spectrum of every sound signal. The parameter which describes the degree of reverberation is the reverberation time. It is time, usually measured in milliseconds, which the reflections need to decay to the level of initial signal minus 60 db. Figure 9: SNR of a noisy recording can be very low, and even negative. Figure 11: Reverberation time. An example of significantly reverberated speech signal is shown in Fig Non linearity of the frequency response Each device in the recording chain has its own amplitude frequency response and works as a filter, changing the initial spectrum of the speech signal. In the output of the recording chain the original signal s spectrum and as follows, the individual features of the voice sample might be significantly changed only because of this effect. An example of such an effect is shown in Fig. 13. Figure 10: Dynamic LPC spectrograms of a clean recording (on top) and a noisy one (at the bottom) Reverberation Reverberation is an effect when the initial signal reflects from different surfaces and these reflected signals mix with the original sound. Thus, reverberated Page 4 of 10

5 frequency band should be limited to the range from 0 Hz to f s /2 Hz, where F s is sampling frequency. Thus, sampling frequency limits the maximum existing frequency in a digital file according to Nyquist theorem. Most common sampling rates are 8000, 11025, 16000, 22050, 32000, Hz etc. It means that the frequency range of the original signal will be limited up to 4000, 5512, 8000, 11025, 16000, Hz etc. respectively. It might cause the elimination of the high formants from the speech signal s spectrum as it is shown in Fig. 14. The bit rate limits the dynamic range of recorded audio signal. Each bit of quantization carries information about 6 db of dynamic range of the signal. Most popular bitrates are: 8-bit, 16-bit, 24-bit etc. Those correspond to 48, 96, 144 db etc. of the dynamic range of the input signal. Wrong bit rate might cause the digital clipping of the speech signal, which will lead to the problems described in the first part of this chapter regarded to overloading. Figure 12: Dynamic FFT spectrograms of a clean recording (on top) and the reverberated one (at the bottom). Figure 14: Average FFT spectrum of a speech signal sampled with 8000Hz. Figure 13: Average FFT spectra of the original sound signal and the same signal recorded through the devices with non-linear amplitude frequency response Sampling frequency and bit rate Sampling frequency and bit rate determine how much information from its initial analogue form will be recorded and saved as a digital audio file. To avoid the aliasing effect analogue signal should be filtered and its 3. REQUIREMENTS FOR SPEAKER ID In the previous chapter five main parameters of speech audio signal were discussed. It was shown that each of them, existing in the signal and having non sufficient value, can make speaker procedure impossible. In this chapter we specify the minimum sufficient values for each of them, which the speech signal should have to be at least acceptable for speaker investigation. To determine these values and find out if there are any rules of their influence on the speech quality we have carried out a series of experiments involving Page 5 of 10

6 forensic experts and automatic voice system Voice Net, produced by Speech Technology Center Ltd. During any type of spectral based speech analysis it is necessary to extract the identical features of the voice. Nowadays they highlight two main identical parameters of the human voice. They are pitch or fundamental frequency and formants or energy maxima in the instantaneous spectrum of the speech signal. Most of the feature extraction algorithms analyze the spectral maxima, according to the amplitude or energy of the different frequencies. Most of the automatic voice systems use Gaussian Mixture Models algorithm for speaker comparison. In this paper we do not make any difference between this algorithm and formants comparison algorithm because they both are based on the spectral maxima and energy distribution. It is well known that in the air the high frequencies decay rapidly than low frequencies, and it leads to the energy difference in the levels of formants. The first formant always has higher amplitude than the second, the second higher than the third etc. It means that any type of changing in this order or changing in the frequency of spectral maxima will lead to the feature extraction mistakes The influence of overloading Clipping is a sort of denomination of the quantity of recorded information. The sounds with levels exceeding dynamic range of the equipment will be recorded with limited amplitude and changed waveform. It was shown in the previous chapter that overloading causes the appearance of the additional frequencies in the spectral representation of the speech signal. In this paragraph we describe the maximum admissible clipping level which does not lead to the false acceptance of fake maxima as the formants or to the significant shift of the formant. Figure 15 shows how different degree of clipping affects each single sine wave in the signal. The difference in amplitude between first (original) harmonic in the spectrum and second (fake) harmonic is around 17 db for 25% of clipping and around 9dB for 95 % of clipping. It means that there is no linear dependence between the clipping degree and the difference in level between original and fake harmonics. The influence of clipping on the real speech signal is shown in Fig. 16. It can be seen without any doubts that dynamic LPC spectrograms and instantaneous LPC spectra of signals clipped more than 25% are significantly changed and it becomes really complicated for both experts and automatic algorithms to find out the real formant s positions especially for high frequency range. Thus, we conclude that fragments of speech signals clipped more than 25% from maximum value are not acceptable for any type of formants based because the positions of formants change significantly because of overloading. However pitch analysis still might be applied to such files. More detailed tests with automatic voice system show that problems start happening when the degree of clipping reaches the level only % of the maximum level of amplitude. There is no way to compensate for the influence of clipping after the recording has been closed. The only way to avoid this effect is proper choosing and adjustment of all equipment in the recording chain. Speech signal has a dynamic nature and the amplitude of such a signal is not constant or stable. It makes this problem smaller for speech analyzers because very often there is a possibility to find the speech fragments without any clipping even in the poor quality files, and take only those for further processing and analysis The influence of noises Noises are additional unwanted components in the signal. They mask useful signals and features in the speech signal. However, in opposite to overloading effect, there are some noise cancellation techniques which allow unmasking of these useful components and identical parameters of the voice. Figure 17 shows an example of noisy speech signals with different SNR: from 55dB to 0dB. It is clearly seen that on the recordings with SNR<10dB it becomes very difficult even for experts to find out the true position of the formants, especially in the high frequency range, where the most individual formants located. More detailed tests with automatic feature extraction algorithms reveal the fact that even SNR=10dB is not always enough to run formant s based analysis, it depends on the type of noise and pronounced sound at the moment. But generally speaking we can conclude that for formants analysis, both experts and automatic systems require the speech samples with minimum level of SNR equal 10dB. Pitch analysis can be accomplished even when this parameter goes down to 0dB, because pitch is the most robust to noises individual feature of the human voice. Page 6 of 10

7 Figure 15: Simple sine wave with different degree of clipping (top row) and corresponding average spectra (bottom row) The influence of reverberation Reverberation is a sort of additional components in the signal. But as an opposite of the noises this type of distortion is very similar to the initial speech signal. This fact makes the anti-reverberation filtering very difficult, and there is still no way to eliminate this effect totally. The requirements for this parameter come from the speed of speech. During any type of speech analysis, the different sounds are to be considered separately. Overlapping of spectrums from different sounds leads to the feature extraction mistakes. From this point of view the maximum time needed for an impulse to decay on 20dB should be no bigger than the duration of average sound. It gives us the maximum value for this parameter around 100ms and 300ms for reverberation time and suppression up to -60dB from the initial value Non linearity of frequency response Non linear frequency response of the recording chain can significantly change the spectrum of the initial speech signal. The requirements for frequency responses and dynamic ranges of the devices are items for standardization and their parameters are usually provided in the corresponding technical specifications. The frequency response of any device used for speech recording should be flat or linear in the frequency range from 100Hz to 5500Hz or at least to 4000Hz for the recordings from phone or radio channels. In [4] we proposed the technique to compensate for the influence of channel or poor frequency response Sampling frequency and bit rate The requirements for these two parameters of the audio file come from the nature of speech. It is well known that maximum frequency of speech signal which can be heard or recorded with existing equipment is around 5500Hz, and in very rare cases it goes a bit higher. It means that for speech analysis and for voice the sampling rate twice bigger than maximum frequency in speech signal would be sufficient. It becomes 11025Hz or 16000Hz for the recordings from microphone channels and 8000Hz for any type of recordings from phone or radio channels. If we use the lower sampling rate we will lose the high frequency range and all highest formants we are most important for speaker. The dynamic range of human speech is less than 96 db, so it makes sense to use 16 bit coding or maximum 24 bit coding for all types of speech analysis or voice. The usage of lower non sufficient bit rate can lead to digital clipping and corresponding side effects during the speech analysis. Page 7 of 10

8 Figure 16: The waveforms of the same speech signal but with different degree of clipping: 0%, 25%, 50%, 75%, 95% respectively from left to right (top row), dynamic LPC spectrograms of corresponding speech signals (middle row) and instantaneous LPC spectra of corresponding fragments with different degree of clipping. Figure 17: The waveforms (top row) and dynamic LPC spectrograms (bottom row) of the same speech signal but with different SNR (55dB, 10dB, 5dB and 0dB from left to right respectively). Page 8 of 10

9 4. VOICE SAMPLE RECORDING This part of the paper provides recommendations and basic rules which must be respected during the voice sample recording. As it was shown in the previous parts of this paper, there are some parameters of speech signal which must have sufficient value to make the voice recording acceptable for speaker examination. All this parameters can be respected on the preparation stage, before the recording process itself. A sort of guidelines or check list is provided below. Following through these steps will guarantee the admissible quality of the voice sample. Preparing the room; Choosing the proper equipment; Instructing the interviewee; Measuring the parameters of recorded signal. The room which is going to be used for voice recording should be prepared in a special way and the requirements to this room are very similar to the requirements to any kind of forensic lab. There are some fundamental works regarded to only this single topic [9]. Here we highlight only some of those critical requirements. Voice recording laboratories are configured or constructed to decrease outside noise and vibration, dampen laboratory equipment sounds, reduce interference from communication transmissions, and minimize electric and magnetic fields. Laboratories are usually located away from noisy indoor and outdoor environments, but if that is not possible, the laboratory walls should be isolated and soundproofed. Equipment and ventilation sounds in the laboratory can be decreased with low-noise fans, enclosed equipment racks, and properly designed and maintained heating and air-conditioning systems. The laboratory should, whenever possible, be located away from radio, television, and other communication transmitters. Cellular telephones, two-way pagers, personal digital assistants (PDAs), and other electronic devices, which transmit and receive information wirelessly, are always turned off in the laboratory. The area should be free of nonessential devices that produce electric and magnetic fields, and whenever possible, incandescent instead of fluorescent lighting should be utilized. If the recording process is going to take place outside of the lab, the maximum number of provided recommendations should be respected and measures taken. Professional microphones, amplifiers, cables, connectors, and equipment racks are part of the standard accessories required for forensic audio laboratory to collect the high quality voice samples. The computer systems used are higher end units with fast CPUs, sufficient RAM and storage, high-speed digital interfaces, large high-resolution monitors. External sound cards are always utilized to avoid the internal noise generated by the computer motherboard, hard drives, and other internal components. All technical specification for all utilized equipment should be read carefully and appropriate settings should be set if needed. The interviewee should be instructed in advance and no attempts to change or mimic the voice should be made. The speed and volume of speech should be kept natural during entire recording process. After the recording has been made it is very important to check the parameters and estimate the quality of speech sample. Each time it is necessary to measure SNR and check amplitude frequency response of the channel. The easiest way to do that is to use the fragment of the signal without useful signal, but pause. In the logarithmic scale the difference between the level of the signal in pause and the level of speech signal would be SNR in decibels and the average spectrum of the signal in pause will give us an idea about frequency response of the recording or transmission chain. To estimate the reverberation time it is better to use the speech signal or another loud signal in the recording and measure the decaying time in the waveform or in the dynamic spectrogram. The effective frequency range of the recording also can be measured from the average spectrum. Most of the problems with the quality of voice samples can be solved in the stage of the recording process with help of high quality equipment and proper adjustment of the tools and techniques which are being used for speech recording. Figure 18: The screenshot of the special software VoicePassport. Page 9 of 10

10 Figure 18 shows the screenshot of the program which estimates major speech sample parameters automatically. It helps the operators to create the high quality voice samples and put them into the database for further comparison. 5. CONCLUSIONS In this paper we have explained the reason of special requirements existing which make the voice recording acceptable for speaker recognition procedures. Law enforcement practitioners can use the materials of this paper to adjust the equipment and prepare the environment to record the high quality voice sample and hard/software developers can use these materials to provide the appropriate tools for the operational people. The main goal of this paper was to provide the law enforcement practitioners with the explanations about important speech signal s parameters and the guidelines, to take the voice samples from the suspects for both manual speaker and for the database of the automatic speaker system. We have described all main speech recording parameters which are vital important for voice performance. We also found out the desired values of these parameters and provided the approaches of measuring them. As the main result of this paper we can highlight the technique of taking the high quality speech sample for further speaker examination. Speech, Language and the Law, 11, pp , [4] A. S. Barinov, S. L. Koval, P.V. Ignatov, M.B. Stolbov, Channel compensation for forensic speaker using inverse processing. Proc. AES 39th International Conference [5] A. Braun, "Speaker - a real challenge for forensic statistics." Proc. Fifth International Conference on Forensic Statistics [6] L.G. Kersta, "Voiceprint Identification," Nature, v.196, pp [7] U.G. Goldstein, "Speaker-identifying features based on formant tracks." J. Acoust. Soc. Am., 59(1), pp , [8] A. S. Barinov, S. L. Koval, P.V. Ignatov, Forensic Speaker Identification based on the Formants Matching Approach. Forensic science international Journal. Paper submitted for publication [9] F.A. Everest, The master handbook of acoustics, 4-th edition. The McGraw-Hill Companies, Inc ACKNOWLEDGEMENTS This work was supported by Speech Technology Center Ltd., Saint Petersburg, Russia. 7. REFERENCES [1] H. Hollien, "The Acoustics of Crime. The New Science of Forensic Phonetics." Plenum Press. New York, [2] P. Rose, "Forensic Speaker Identification." London, New York: Taylor & Francis, [3] C. Byrne and P. Foulkes, The mobile phone effect on vowel formants, The International Journal of Page 10 of 10

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