Abstract:
A method and system detect speech activity. An ultrasonic signal is directed at a face of a speaker over time. A Doppler signal of the ultrasonic signal is acquired after reflection by the face. Energy in the Doppler signal is measured over time. The energy over time is compared to a predetermined threshold to detect speech activity of the speaker in a concurrently acquired audio signal.

Description:
FIELD OF THE INVENTION 
   The invention relates generally to speech-based user interfaces, and more particularly to hands-free interface. 
   BACKGROUND OF THE INVENTION 
   A speech-based user interface acquires speech input from a user for further processing. Typically, the speech acquired by the interface is processed by an automatic speech recognition system (ASR). Ideally, the interface responds only to the user speech that is specifically directed at the interface, but not to any other sounds. 
   This requires that the interface recognizes when it is being addressed, and only responds at that time. When the interface does accept speech from the user, the interface must acquire and process the entire audio signal for the speech. The interface must also determine precisely the start and the end of the speech, and not process signals significantly before the start of the speech and after the end of the speech. Failure to satisfy these requirements can cause incorrect or spurious speech recognition. 
   A number of speech-based user interfaces are known. These can be roughly categorized as follows. 
   Push-to-Talk 
   With this type of interface, the user must press a button only for the duration of the speech. Thus, the start and end of speech signals are precisely known, and the speech is only processed while the button is pressed. 
   Hit-to-Talk 
   Here, the user briefly presses a button to indicate the start of the speech. It is the responsibility of the interface to determine where the speech ends. As with push-to-talk interface, the hit-to-talk interface also attempts to ensure that speech is only when the button is pressed. 
   However, there are a number of situations where the use a button may be impossible, inconvenient, or simply unnatural, for example, any situation where the user&#39;s hands are otherwise occupied, the user is physically impaired, or the interface precludes the inclusion of a button. Therefore, hands-free interfaces have been developed. 
   Hands-Free 
   With hands-free speech-based interfaces, the interface itself determines when speech starts and ends. 
   Of the three types of interface, the hands-free interface is arguably the most natural, because the interface does not require an express signal to initiate or terminate processing of the speech. In most conventional hands-free interfaces, only the audio signal acquired by the primary sensor, i.e., the microphone, is analyzed to make start and end of the speech decisions. 
   However, the hands-free interface is the most difficult to implement because it is difficult to determine automatically when the interface is being addresses by just the user, and when the speech starts and ends. This problem becomes particularly difficult when the interface operates in a noisy or reverberant environment, or in an environment where there is additional unrelated speech. 
   One conventional solution uses “attention words.” The attention words are intended to indicate expressly the start and/or end of the speech. Another solution analyzes an energy profile of the audio signal. Processing begins when there is a sudden increase in the energy, and stops when the energy decreases. However, this solution can fail in a noisy environment, or an environment with background speech. 
   A zero crossing rates of the audio signal can also be used. The zero-crossings occur when the speech signal changes between positive and negative. When the energy and zero-crossings are at predetermined levels, speech is probably present. 
   Another class of solutions uses secondary sensors to acquire secondary measurements of the speech signal, such as a glottal electromagnetic sensor (GEMS), a physiological microphone (P-mic), a bone conduction sensors, and an electroglottographs. However all of the above secondary sensors need to be mounted on the user of the interface. This can be inconvenient in any situation where it is difficult to forward the secondary signal to the interface. That is, the user may need to be ‘tethered’ to the interface. 
   An ideal secondary sensor for a hands-free, speech-based interface should be able to operate at a distance from the user. Video cameras could be used as effective far-field sensors for detecting speech. Video images can be used for face detection and tracking, and to determine when the user is speaking. However, cameras are expensive, and detecting faces and recognizing moving lips is tedious, difficult and error prone. 
   Another secondary sensor uses the Doppler effect. An ultrasonic transmitter and receiver are deployed at a distance from the user. A transmitted ultrasonic signal is reflected by the face of the user. As user speaks parts of the face move, which changes the frequency of the reflected signal. Measurements obtained from the secondary sensor are used in conjunction with the audio signal acquired by the primary sensor to detect when the user speaks. 
   In addition to being usable at a distance from the user, the Doppler sensor differs from conventional secondary sensors in another, crucial way. The measurements provided by conventional current secondary sensors are usually linearly related to the speech signal itself. The GEMS sensor provides measurements of the excitation function to the vocal tract. The signals acquired by P-mics, throat microphones and bone-conduction microphones are essentially a filtered versions of the speech signal itself. 
   In contrast, the signal acquired by the Doppler sensor is not linearly related to the speech signal. Rather, the signal expresses information related to the movement of the face while speaking. The relationship between facial movement and the speech is not obvious, and certainly not linear. 
   However, the Doppler sensors use a support vector machine (SVM) to classify the audio signal as speech or non-speech. The classifier must first be trained off-line on joint speech and Doppler recordings. Consequently, the performance of the classifier is highly dependent on the training data used. It may be that different speakers articulate speech in different ways, e.g., depending on gender, age, and linguistic class. Therefore, it may be difficult to train the Doppler-based secondary sensor for a broad class of users. In addition, that interface requires both a speech signal and the Doppler signal for speech activity detection. 
   Therefore, it desired to provide a speech activity sensor that does not require training of a classifier. It is also desired to detect speech only from the Doppler signal, without using any part of the concomitant audio signal. Then, as an advantage, the detection process can be independent of background “noise,” be it speech or any other spurious sounds. 
   SUMMARY OF THE INVENTION 
   The embodiments of the invention provide a hands-free, speech-based user interface. The interface detects when speech is to be processed. In addition, the interface detects the start and end speech so that proper segmentation of the speech can be performed. Accurate segmentation of speech improves noise estimation and speech recognition accuracy. 
   A secondary sensor includes an ultrasonic transmitter and receiver. The sensor detects facial movement when the user of the interface speaks using the Doppler effect. Because speech detection can be entirely based only on the secondary signal due to the facial movement, the interface works well even in extremely noisy environments. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of a hands-free speech-based user interface according to an embodiment of our invention; 
       FIG. 2  is a flow diagram of a method for detecting speech activity using the interface of  FIG. 1 ; and 
       FIGS. 3A-3C  are timing diagrams of primary and secondary signals acquired and processed by the interface of  FIG. 1  and the method of  FIG. 2 . 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
   Interface Structure 
   Transmitter 
     FIG. 1  shows a hands-free, speech-based interface  100  according to an embodiment of our invention. Our interface includes a transmitter  101 , a receiver  102 , and a processor  200  executing the method according to an embodiment of the invention. The transmitter and receiver, in combination, form an ultrasonic Doppler sensor  105  according to an embodiment of the invention. Hereinafter, ultrasound is defined as sound with a frequency greater than the upper limit of human hearing. This limit is approximately 20 kHz. 
   The transmitter  101  includes an ultrasonic emitter  110  coupled to an oscillator  111 , e.g., 40 kHz oscillator. The oscillator  111  is a microcontroller that is programmed to toggle one of its pins, e.g., at 40 kHz with a 50% duty cycle. The use of a microcontroller greatly decreases the cost and complexity of the overall design. 
   In one embodiment, the emitter has a resonant carrier frequency centered at 40 kHz. Although the input to the emitter is a square wave, the actual ultrasonic signal emitted is a pure tone due to a narrow-band response of the emitter. The narrow bandwidth of the emitted signal corresponds approximately to the bandwidth of a demodulated Doppler signal. 
   Receiver 
   The receiver  102  includes an ultrasonic channel  103  and an audio channel  104 . 
   The ultrasonic channel includes a transducer  120 , which, in one embodiment, has a resonant frequency of 40 kHz, with a 3 dB bandwidth of less than 3 kHz. The transducer  120  is coupled to a mixer  140  via a preamplifier  130 . The mixer also receives input from a band pass filter  145  that uses, in one embodiment, a 36 KHz signal generator  146 . The output of the mixer is coupled to a first low pass filter  150 . 
   The audio channel includes a microphone  160  coupled to a second low pass filter  170 . The audio channel acquires an audio signal. Hereinafter, an audio signal specifically means an acoustic signal that is audible. In a preferred embodiment, the audio channel is duplicated so that a stereo audio signal can be acquired. 
   Outputs  151  and  171  of the low pass filters  150  and  170 , respectively, are processed  200  as described below. The eventual goal is to detect only speech activity  181  by a user of the interface in the received audio signal. 
   The transmitter  110  and the transducer  120  in the preferred embodiment have a diameter of approximately 16 mm, which is nearly twice the wavelength of the ultrasonic signal at 40 kHz. As a result, the emitted ultrasonic is spatially narrow beam, e.g., with a 3 dB beam width of approximately 30 degrees. This makes it possible for the ultrasonic signal to be highly directional. This decreases the likelihood of sensing extraneous signals not associated with facial movement. In fact, it makes sense to colocate the transducer  120  with the microphone  160 . 
   Most conventional audio signal processors cut off received acoustic signals well below 40 kHz prior to digitization. Therefore, we heterodyne the received ultrasonic signal such that the resultant much lower “beat frequency” signal falls is within the audio range. Doing so also provides us with another advantage. The heterodyned signal can be sampled at audio frequencies, with the additional benefits in a reduction of computational complexity. 
   The signal  121  acquired by the transducer is pre-amplified  130  and input to the analog mixer  140 . The second input to the mixer is a 36 kHz, as in our preferred embodiment, sinusoid signal. The sinusoid signal is generated by producing a 36 kHz 50% duty cycle square wave from the microcontroller. The square wave is bandpass filtered  145  with a fourth order active filter. The output of the mixer is then low-pass filtered  150  with a cutoff frequency of 8 kHz, as in our preferred embodiment. 
   The audio channel includes a microphone  160  to acquire the audio signal. In preferred embodiment, the microphone is selected to have a frequency response with a 3 dB cutoff frequency below 8 kHz. This ensures that the audio channel does not acquire the ultrasonic signal. The audio signal is further low-pass filtered by a second order RC filter  170  with a cut off frequency of 8 kHz. 
   The outputs  151  and  171  of the ultrasonic channel and the audio channel are jointly fed to the processor  200 . The stereo signal is sampled at 16 kHz before the processing  200  to detect the speech activity  181 . 
   Interface Operation 
   The ultrasonic transmitter  101  directs a narrow-beam, e.g., 40 kHz, ultrasonic signal at the face of the user of the interface  100 . The signal emitted by the transmitter is a continuous tone that can be represented as s(t)=sin( 2 πf c t), where f c , is the emitted frequency, e.g., 40 kHz in our case. 
   The user&#39;s face reflects the ultrasonic signal as a Doppler signal. Herein, the Doppler signal generally refers to the reflected ultrasonic signal. While speaking, the user moves articulatory facial structures including but not limited to the mouth, lips, tongue, chin and cheeks. Thus, the articulated face can be modeled as a discrete combination of moving articulators, where the i th  component has a time-varying velocity v i (t). The low velocity movements cause changes in wavelength of the incident ultrasonic signal. A complex articulated object, such as the face, exhibits a range of velocities while in motion. Consequently, the reflected Doppler signal has a spectrum of frequencies that is related to the entire set of velocities of all parts of the face that move as the user speaks. Therefore, as stated above, the bandwidth of the ultrasonic signal corresponds approximately to the bandwidth of frequencies at which the facial articulators move. 
   The Doppler effect states that if a tone of frequency f is incident on an object with velocity v relative to a sensor  120 , the frequency {circumflex over (f)} of the reflected Doppler signal is given by 
                     f   ^     =             v   s     +   v         v   s     -   v       ⁢   f     ≈       (     1   +       2   ⁢   v       v   s         )     ⁢   f         ,           (   1   )               
where v s  is the speed of sound in a particular medium, e.g., air. The approximation to the right in Equation (1) holds true if v&lt;&lt;v s , which is true for facial movement.
 
   The various articulators have different velocities. Therefore, each articulator reflects a different frequency. The frequencies change continuously with the velocity of the articulators. The received ultrasonic signal can therefore be considered as sum of multiple frequency modulated (FM) signals, all modulating the same carrier frequency (f c ). The FM can be modeled as: 
                     d   ⁡     (   t   )       =       ∑   i     ⁢       a   i     ⁢     sin   ⁡     (       2   ⁢           ⁢   π   ⁢           ⁢       f   c     ⁡     (     t   +       2     v   s       ⁢       ∫   0   t     ⁢         v   i     ⁡     (   τ   )       ⁢     ⅆ   τ             )         +     ϕ   i       )             ,           (   2   )               
where V i (τ) is the velocity at a specific instant of time ‘τ’.
 
   Equation (2) uses the approximate form of the Doppler Equation (1). The variable a i  is the amplitude of the signal reflected by the i th  articulated component. This variable is related to the distance of the component from the sensor. Although a i  is time varying, the changes are relatively slow, compared to the sinusoidal terms in Equation 2. We assume the term to be a constant gain term. 
   The variable φ i  is a phase term intended to represent relative phase differences between the Doppler signals reflected by the various moving articulators. If f c  is the carrier frequency, then Equation (2) represents the sum of multiple frequency modulated (FM) signals, all operating on the single carrier frequency f c . 
   Most of the information relating to the movement of facial articulators resides in the frequency of the signals in Equation (1). In preferred embodiment, we demodulate the signal such that this information is also expressed in the amplitude of the sinusoidal components, so that a measure of the energy of these movements can be obtained. 
   Conventional FM demodulation proceeds by eliminating amplitude variations through hard limiting and band-pass filtering, followed by differentiating the signal to extract the ‘message’ into the amplitude of the sinusoid signal, followed finally by an envelope detector. 
   Our FM demodulation is different. We do not perform the hard-limiting and band-pass filtering operation because we want to retain the information in the amplitude α i . This gives us an output that is more similar to spectral-decomposition of the ultrasonic signal. 
   The first step differentiates the received ultrasonic signal d(t). From Equation (2) we obtain 
   
     
       
         
           
             
               
                 
                   
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   A low-pass filter with a cut-off below f c  cut off the second sinusoid on the right in Equation 4 finally giving us: 
                     L   ⁢           ⁢   P   ⁢           ⁢     F   ⁡     (       sin   ⁡     (     2   ⁢           ⁢   π   ⁢           ⁢     f   c     ⁢   t     )       ⁢     ⅆ     ⅆ   t       ⁢     d   ⁡     (   t   )         )         =     -       ∑   i     ⁢     2   ⁢           ⁢   π   ⁢           ⁢     a   i     ⁢       f   c     ⁡     (     1   +       2   ⁢       v   i     ⁡     (   t   )           v   s         )       ⁢     sin   ⁡     (           2   ⁢           ⁢   π   ⁢           ⁢     f   c         v   s       ⁢       ∫   0   t     ⁢         v   i     ⁡     (   τ   )       ⁢     ⅆ   τ           +     ϕ   i       )               ,           (   5   )               
where LPF represents the low-pass-filtering operation.
 
   The signal represented by Equation (5) encodes velocity terms in both amplitudes and frequencies. If the signal is analyzed using relatively short analysis frames, the velocities of the frequencies do not change significantly within a particular analysis frame, and the right hand side of Equation (5) can be interpreted as a frequency decomposition of the left hand side. 
   The signal contains energy primarily at frequencies related to the various velocities of the moving articulators. The energy at any velocity is a function of the number and distance of facial articulators moving with that velocity, as well as the velocity itself. 
   Speech Activity Detection 
     FIG. 2  shows the method  200  for speech activity detection according to an embodiment of the invention. The ultrasonic Doppler signal  151  and the audio signal  171  acquired by the ADS  105  are both sampled  201  at 16 kHz.  FIG. 3A  shows the reflected Doppler signal. In  FIGS. 3A-3B , the vertical axis is amplitude.  FIG. 3C  also shows the normalized energy contour of the Doppler signal. The horizontal axis is time. 
   The signals are then partitioned  210  into frames using, e.g., a 1024 point Hamming window. 
   The audio signal  171  is processed only while speech activity  181  from the user is detected. 
   Facial articulators are relatively slowly moving. The frequency variations due to their velocity are low. The ultrasonic signal is demodulated 220 into a range of frequency range, e.g., 25 Hz to 150 Hz. Frequencies outside this range, although potentially related to speech activity, are usually corrupted by the carrier frequency, as well as harmonics of the speech signal including any background speech or babble, particularly in speech segments.  FIG. 3B  shows the demodulated Doppler signal. 
   To obtain the frequency resolution needed for analyzing the ultrasonic signal, the frame size is a relatively large, e.g., 64 ms. Each frame includes 1024 samples. Adjacent frames overlap by 50%. 
   From each frame of the demodulated and windowed Doppler signal, we extract 230 discrete Fourier transform (DFT) coefficients for eight bins in a frequency range from 25 Hz to 150 Hz. In our preferred implementation, we actually use the well known Goertzel&#39;s algorithm, see e.g., U.S. Pat. No. 4,080,661 issued to Niwa on Mar. 21, 1978, “Arithmetic unit for DFT and/or IDFT computation,” incorporated herein by reference. 
   The energy in these frequency bands is determined from the DFT coefficients. Typically, the sequence of energy values is very noisy. Therefore, we “smooth”  240  the energy using a five point median filter. 
     FIG. 3C  shows the energy contour as well as the audio signal. The Figure shows that the energy in the Doppler signal is correlated to speech activity. 
   To determine if the t th  frame of audio signal represents speech, the median filtered energy value E d ( t ) of the Doppler signal in the corresponding frame is compared  250  to an adaptive threshold β t  to determine whether the frame indicates speech activity  202 , or not  203 . The threshold for the t th  frame is adapted as follows:
 
β t =β t−1 +μ( E   d ( t )− E   d ( t− 1)),
 
where μ is an adaptation factor that can be adjusted for optimal performance.
 
   If the frame is not indicative of speech, then we assume an end of an utterance  260  event. An utterance is defined as a sequence of one or more frames of speech activity followed by a frame that is speech. The energy E c  of the current audio frame  204  and the energy E p  of the last confirmed frame  289  that includes speech are compared  285  according to αE p ≦E c . The scalar α is a selectable non-speech parameter between 0 and 1 to determine speech and non-speech frames  291 - 292 , respectively. 
   This event initiates end of speech detection  270 , which operates only on the audio signal. The method continues  275  to detect speech up to three frames after the end of utterance event. Finally, adjacent speech segments that are within 200 ms of each other are merged. 
   EFFECT OF THE INVENTION 
   The interface according to the embodiments of the invention detects speech only when speech is directed at the interface. The interface also concatenates adjacent speech utterances. The interface excludes non-speech audio signals. 
   The ultrasonic Doppler sensor is accurate at SNRs as low as −10 dB. The interface is also relatively insensitive to false alarms. 
   The interface has several advantages. It is inexpensive, has low false trigger rate and is not affected by ambient out-of-band noise. Also, due to the finite range of the ultrasonic receiver, the output is not affected by distant movements. 
   The interface only uses the Doppler signals to make the initial decision whether speech activity is present or not. The audio signal can be used optionally to concatenate adjacent short utterance into continuous speech segments. 
   Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.