SPEECH RECOGNITION USING LPC AND HMM APPLIED FOR CONTROLLING MOVEMENT OF MOBILE ROBOT

Transcription

1 Seinar asiona Teknoogi Inforasi 200 SPEECH RECOGITIO USIG LPC AD HMM APPLIED FOR COTROLLIG MOVEMET OF MOBILE ROBOT Thiang ) Wanto ) ) Eectrica Engineering Departent Petra Christian university Siwaankerto 2-3, Surabaya Indonesia eai : thiang@petra.ac.id ABSTRACT This paper describes about a proect of speech recognition, which is appied on a obie robot for controing oveent of the robot. The ethods that used in this proect are Linear Predictive Coding (LPC) and Hidden Markov Mode (HMM). LPC ethod is used to extract data of speech, on the other hand HMM is used to recognize the unknown speech pattern. This syste is ipeented on persona coputer. Syste wi take the input speech pattern for second. Speech pattern wi be processed with LPC ethod. LPC output wi becoe the input of HMM ethod to find λ ode for every coand. The biggest observation sybo probabiity wi deterine the recognized word. Based on the experient, the highest recognition rate that can be achieved is 00 % when using a icrophone as the input interface, nuber of sape is 5, nuber of observation sybo are 40, 45, 50. Mobie robot can aso ove according to the input coand. Key words speech recognition, LPC, HMM, obie robot. Introduction Autoatic speech recognition by achine has been a goa of research for ore than four decades. However, in spite of the gaour of designing an inteigent achine that can recognize the spoken word and coprehend its eaning, and in spite of enorous research efforts spent in trying to create such a achine, it is far fro achieving the desired goa of a achine that can understand spoken discourse on any subect by a speakers in a environents []. The speech recognition syste has aso been ipeented on soe particuar devices. Soe of the are persona coputer (PC), digita signa processor, and another kind of singe chip integrated circuit. A fraework to address the quantization issues which arise in fixedpoint isoated word recognition was introduced in [2]. The syste was deveoped using C++ anguage which is ipeented on a PC. Reference [3] describes a speech recognition syste using SPHIX-II, an off-the-shef speech recognition package [4]. In reference [5] and [6], speech recognition syste has been tried to be ipeented on a FPGA and an ASIC. In reference [7], the speech recognition syste was ipeented on MCS5 icrocontroer. The ethod used to recognize the word in a speech signa is Linear Predictive Coding (LPC) cobined with Eucidean Squared Distance. The highest recognition rate that can be achieved is about 78.57%. ext, in reference [8], the speech recognition was ipeented on another icrocontroer. The icrocontroer where the speech recognition was ipeented on is ATega62. The highest recognition rate that can be achieved is about 87%. This paper describes further work about the speech recognition which was ipeented on a PC. The ethods ipeented on this syste are LPC and HMM. LPC is used as extraction ethod and HMM is used as the recognition ethod. Besides ipeenting on a PC, another difference with previous work [8] is coand that used for controing oveent of the robot consists of ore than one word. A the coands that are used to contro oveent of the robot are Indonesian anguage, such as Mau Satu. Mau Dua, Kanan, Kiri, Stop, etc. 2. Hardware Syste of Mobie Robot Bock diagra of the hardware syste of obie robot is shown at figure. Voice signa is inputted through a icrophone. Because the output votage of icrophone is too sa, then output of icrophone is apified using an apifier circuit with gain about 00. Output of the

2 icrophone is aso fitered by using a band pass fiter circuit with bandwidth of Hz. A Microcontroer is used to sape the voice signa. The voice signas are saped using frequency saping of 8 khz. And the voice coand is iited to second. The saped voice signa is then sent to persona coputer for signa processing and recognition process. A the signa processing ethod and recognition ethod are ipeented on the persona coputer. The sae icrocontroer wi receives recognition resut fro persona coputer and then contro the oveent of the robot according to the recognition resut. By using HMM structure shown in the figure 3, a the paraeters are known except the observation sybo probabiity distributions (B) which vary depending on the training data set. Therefore, in this particuar HMM training syste, ony observation sybo probabiity distributions in the state are cacuated. The others paraeters are pre-defined. Firsty, the speech signa is processed by using LPC processor and resut the LPC cepstra coefficients. The basic steps of LPC processor incude the foowing [][9]:. Preephasis: The digitized speech signa, s(n), is put through a ow order digita syste, to spectray fatten the signa and to ake it ess susceptibe to finite precision effects ater in the signa processing. The output of the preephasizer network,, is reated to the input to the network, s (n), by difference equation: ~ s ( n) s( n) as ~ ( n ) (2) The ost coon vaue for a ~ is around Figure 2. Bock Diagra of HMM Training Syste 3. Design Speech Syste The designed speech recognition syste uses two ethods. They are Linear Predictive Coding (LPC) and Hidden Markov Mode (HMM). LPC is used feature extraction ethod and HMM is used as the recognition ethod. Generay, the syste is divided into two parts. The first part is training syste and the second part is recognition syste. Bock diagra of HMM training syste is shown at figure 2. The ain task of the HMM training syste is to create HMM λ ode of each word sape. Paraeters of HMM λ ode consist of nuber of state (), nuber of observation sybo (M), state transition probabiity distribution (A), observation sybo probabiity distribution (B), and initia state probabiity distribution (π). Figure 2. Bock Diagra of HMM Training Syste 2. Frae Bocking: The output of preephasis step, ~ s ( n ), is bocked into fraes of sapes, with adacent fraes being separated by M sapes. If th x (n) is the frae of speech, and there are L fraes within entire speech signa, then x ~ ( n) s ( M + n) (3) where n 0,,, and 0,,,L 3. Windowing: After frae bocking, the next step is to window each individua frae so as to iniize the signa discontinuities at the beginning and end of each frae. If we define the window as w(n), 0 n, then the resut of windowing is the signa: ~ x ( n) x ( n) w( n ) where 0 n (4) Typica window is the Haing window, which has the for 2πn w( n) cos 0 n (5) 4. Autocorreation Anaysis: The next step is to auto correate each frae of windowed signa in order to give Figure 3. States Left-Right HMM Mode r ( ) ~ x n 0 ( n) ~ x ( n + ) 0,,,p (6)

3 where the highest autocorreation vaue, p, is the order of the LPC anaysis. 5. LPC Anaysis: The next processing step is the LPC anaysis, which converts each frae of p + autocorreations into LPC paraeter set by using Durbin s ethod. This can foray be given as the foowing agorith: Bock diagra of HMM recognizer syste is shown at figure 4. Sae as in the HMM training syste, firsty, the speech signa of an unknown word is converted to the observation sequence of that word. This is done by perforing the LPC processor and vector quantization process. Then, the observation sequence probabiity of unknown word for a given ode λ ( P ( O λ )) is cacuated for each HMM word ode. (0) E r(0) (7) i i r( i) α r( i ) ki i E i p (8) α i ki (9) ( i ) ( i ) α α k α i- (0) i i 2 i E ( ki ) E () By soving (7) to () recursivey for i,2,,p, the LPC coefficient, a, is given as a α (2) ( p) 6. LPC Paraeter Conversion to Cepstra Coefficients: LPC cepstra coefficients, is a very iportant LPC paraeter set, which can be derived directy fro the LPC coefficient set. The recursion used is k c a + ck a p (3) k k k c ck a > p (4) k k p Vector quantization is appied to the each cepstra coefficients and resut the observation sybo of each state. The sequences of observation sybo of state, state 2, up to state represent the observation sequence of the word. If nuber of sapes per word that are trained in the HMM speech recognition syste is R, and each sape is converted to the observation sequence by the feature extraction and vector quantization process, the observation sybo probabiity distribution (B) in a state can be cacuated by using foowing equation: b n ( vk ) ( k) (5) where b ( k ) R is probabiity of observation sybo v in the k state, n ( v k ) is nuber of observation sybo v in the k state, and R is nuber of sape. Figure 4. Bock Diagra of HMM Recognizer Syste The probabiity of observation sequence cacuation is done by using the forward procedure ethod. For the designed HMM structure (figure 2), the probabiity of the observation sequence, O (o o 2 o ), for the given ode can be cacuated by using the foowing equation: P ( O ) b ( o ) b ( o ) b ( o ) L ( o ) where ( ) ( o λ (6) b o is probabiity of -th observation sybo )in state. Equation (6) is used to cacuate the observation sequence probabiity of unknown speech signa and the unknown speech signa wi be recognized as HMM word ode which has the axiu probabiity of observation sequence ( P ( O λ) ). 4. Experienta Resut Experients were done in severa variations of nuber of observation sybo. The experients were aso done in various nubers of sapes as the training data set. The syste was tested using voice which is saped directy fro icrophone. b

4 Tabe shows suary of experienta resuts of the syste using 5 sapes training data for each coand respectivey. Experienta resuts of the syste using 0, 5, and 20 sapes training data are shown at the tabe 2, 3, and 4 respectivey. The experients were done using syste with nuber of observation sybo varies fro 5 to 50. Tabe Suary of Experienta Resuts of the Syste Using 5 sapes per coand. uber of Observation Sybo 5 20% 0 85% 5 95% % % % % % % % Tabe 2 Suary of Experienta Resuts of the Syste Using 0 sapes per coand. uber of Observation Sybo % % % % % % % % % Tabe 3 Suary of Experienta Resuts of the Syste Using 5 sapes per coand. uber of Observation Sybo % % % % % 30 85% % 40 00% 45 00% 50 00% Tabe 4 Suary of Experienta Resuts of the Syste Using 20 sapes per coand. uber of Observation Sybo % % % % % % % 40 90% 45 90% % Fro tabe to 4, we can see that the best resut was achieved by the syste using 5 sapes training data for each coand and observation sybos. We can aso see that if we ony increase the nuber of sapes then the recognition rate does not aways increase. It eans that increasing nuber of sapes does not aways give better recognition rate if we do not increase the nuber of observation sybo of HMM. Better recognition rate can be achieved by increasing the nuber of sapes and nuber of observation sybo. For this case, better recognition rate was achieved by the syste if the syste uses nuber of observation sybo greater than 0. A experients were done in conditions where the environent is sient and it needs consistent utterance. Therefore this syste sti has drawback in noisy environent. Experienta resuts aso show that the obie robot can ove according to the input coand if the recognition syste gives correct resut. So it ony depend on the recognition rate of the syste. 5. Concusion Fro experienta resuts, it can be concuded that the designed speech recognition syste can recognize the voice coand we. The best recognition rate that can be achieved by the syste is 00% and it is achieved by the syste using 5 sapes per coand and observation sybos of HMM. This achieveent is reach with condition sient environent and consistent utterance. These conditions are drawback of the syste. For further deveopent, it is necessary to iprove the syste so that it can run we in the noisy environent. It ust robust to the noisy environent.

5 REFERESI [] Lawrence Rabiner, and Biing Hwang Juang, Fundaentas of Speech. Prentice Ha, ew Jersey, 993 [2] Y.M. La, M.W. Mak, and P.H.W. Leong, Fixed point ipeentations of Speech Systes. Proceedings of the Internationa Signa Processing Conference. Daas [3] Soshi Iba, Christiaan J. J. Paredis, and Pradeep K. Khosa. Interactive Mutioda Robot Prograing. The Internationa Journa of Robotics Research (24), pp 83 04, 2005 [4] Huang, X. et a. The SPHIX-II speech recognition syste: an overview. Coputer Speech and Language 7(2): pp 37 48, 993. [5] Treeunuk, Dusadee. (200). Ipeentation of Speech on FPGA. (Masters research study, Asian Institute of Technoogy, 200). Bangkok: Asian Institute of Technoogy. [6] Sriharuksa, Janwit. (2002). An ASIC Design of Rea Tie Speech. (Masters research study, Asian Institute of Technoogy, 2002). Bangkok: Asian Institute of Technoogy. [7] Thiang, Ipeentation of Speech on MCS5 Microcontroer for Controing Wheechair, Proceedings of Internationa Conference on Inteigent and Advanced Syste. Kuaa Lupur, Maaysia, [8] Thiang, Dhanny Wiaya, Liited Speech for Controing Moveent of Mobie Robot Ipeented on ATega62 Microcontroer, Proceedings of Internationa Conference on Coputer and Autoation Engineering, Bangkok, Thaiand, [9] Ethnicity Group. Cepstru Method cepstru/cepstru.ht.