ON THE PERFORMANCE OF WTIMIT FOR WIDE BAND TELEPHONY

Transcription

1 ON THE PERFORMANCE OF WTIMIT FOR WIDE BAND TELEPHONY D. Nagajyothi 1 and P. Siddaiah 2 1 Department of Electronics and Communication Engineering, Vardhaman College of Engineering, Shamshabad, Telangana, India 2 Department of Electrical and Computer Engineering, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur, India nagajyothi1998@gmail.com ABSTRACT WTIMIT, which is a derivative of TIMIT emerged as a latest technique for speech quality. The technique has good wideband characteristics over a range of 50-7 KHz. in this paper, a study on the performance of phoneme recognition system has been performed. The study includes the effect of decimating the signal to 8 KHz in the conventional case. Further it is possible to evaluate the AMR-wideband codec for several acoustic models. It is possible to propose the WTIMIT type of wideband channel data from training interactive voice receiving system. Keywords: speech codecs, IVR, AMR-Wb, TIMIT. 1. INTRODUCTION The Typical Bandwidth of Speech is less than 4 KHz for applications in telephonic operations. This is often termed as narrow band. The IVR operates at a sampling rate of 8 KHz for Operation [1-3]. Citing this, the advanced speech service system expanded their BW to wideband (WB) frequency range of KHz. The influence of a conventional telephony network on the N- TIMIT is used to evaluate the performance features of recognition system in traditional telephony. The Phoneme error rate (PER) suppressed by a huge extent due to direct WB speech. Similar in NB case, 23% relative PER degradations is identified. It is also reported that there is an evidence of the impact of a WB mobile network using the WTIMIT corpus. An enhancement of 19% PER with respect to direct WB speech is observed while there is a suppressing 3% PER relative to narrow band. In spite of these efforts it is to note that investigation pertaining to effects of telephony network are to be validated. This is more desirous in IVR based Telephony system. The development of DARPA TIMIT Acoustic Phonetic Continuous Speech Corpus paved a way for evaluating automatic speech recognition (ASR) systems [4]. It constitutes wideband speech recordings which are sampled at 16 KHz. They typically containing in the rate of 50 Hz to 7 khz with respect to 630 native speakers. This is with reference to 8 major regions in the US. For training ten phonetically rich sentences are collected from every speech. In every utterance several features are extracted along with speech waveform, time aligned orthographic, phonetic and word transcriptions are taken. With reference to these efforts as of now there are five TIMIT derivatives namely FFMTIMIT, NTIMIT, CTIMIT, HTIMIT and STC-TIMIT. The FFMTIMIT can be abbreviated as Free Field Microphone TIMIT typical composed of natural TIMIT database. It typically uses a free field device for recording. NTIMIT (Network TIMIT) is adjunct to TIMIT with database constituting the speech wave form [4]. Over a telephone handset, Similarly CTIMIT constitutes of the original TIMIT recordings were passed through cellular telephone circuits. However, in the case of HTIMIT (Handset TIMIT) the data base consists of two subset with 192 male and 192 female speakers [5]. The corresponding speech signals are those which are transmitted through different telephone handsets. This typically helps in the investigation of telephone transducer effects on speech. For STCTIMIT which is single channel, the speech signals were sent through a real and, in contrast to NTIMIT, all these can be turned as the derivation of wideband speech [6-11]. While some are telephony are containing narrowband speech. The sampling is at the rate of 8 khz with a range of 200 Hz to 3.4 khz. Inspite of all these it is to be noted that there is no availability of real world wideband telephony speech corpus. Several versions of wideband speech codes like G.722 (1988), G.722 (1999) G (2001) and G (2008) have been into operation with several techniques like ADPCM, 3GPP [6] and wide band PCM. It is interesting to note that the wide band telephony speech transmission system is wide available and adaptable. In contrast to ever increasing mobile networks citing this, it its essential to have wideband system in the TIMIT for a wide range of scientific investigations. There are several advantages and applications associated with WBSTS. The integrated speech recognition system provides remote dictation or spelling. This was not a possible case with the earlier telephony system. In this paper, an investigation on the performance of the speech CODECS in terms of Bit rates is performed. The analysis is based experimentation carried out in MATLAB on windows platform in an i3 with 4 GB RAM. Further, the paper is organized as follows. A brief discussion on the standards and the corresponding bit rates of several speech codecs are presented in section 2. Results pertaining to synthesis followed by analysis of speech codecs is given section 3. Overall conclusion is given in section SPEECH CODECS In this Section, a brief introduction to the speech codecs is given. The aim of the speech codec is to compress the speech signal in order to reduce the bandwidth and requires minimum storage space. When we will reconstruct, it must be very close to original one. 1386

2 Based on intelligibility and naturalness we will measure perceived quality of the signal. Here we have considered two types of the networks GSM and VoIP. Table-1 and Table-2 shows the specification summary of the all the supported narrowband wideband codecs. Table-1. ITU-T approved VoIP supported narrowband and wideband speech codecs. Coding standard Algorithm Sampling frequency (khz) Bit rates (kbps) G.711 (A /U) Companded PCM 8 64 G.726 ADPCM 8 16/24/ 32/40 G.729 CS-ACELP 8 8 G.723.1A ACELP / MP-MLQ / 6.3 G.722 (WB) SB-ADPCM 16 48, 56, 64 G (WB) Companded PCM, MDCT 16 64, 80, 96 G (WB) CELP, TD-BWE, TDAC G (AMR-WB) (Multi Rate) MRWB- ACELP , 8.85, 12.65, 14.25, 15.85, 18.25, 19.85, 23.05, Table-2. ETSI/3GPP approved GSM supported narrowband and wideband speech codecs. Coding standard Algorithm Sampling frequency (khz) Bit rates (kbps) GSM FR RPE-LTP 8 13 GSMEFR ACELP GSM HR VSELP (Multi Rate) MR- 4.75, 5.15, 5.90, 6.70, 7.40, GSM AMR 8 ACELP 7.95, 10.2, , 8.85, 12.65, 14.25, 15.85, GSM AMR-WB MRWB-ACELP , 19.85, 23.05, RESULTS AND DISCUSSIONS Results pertaining to the technique and proposed method are presented in this Section. Testing of the Coded Data with G.711-Coded Models (8-kHz HMMs). The 8- khz un-coded and coded speech data that is coded with all other wireline codecs, such as G.711, G.726 and G.729, is tested with the G.711-coded models (8-kHz trained HMMs) for the CI, and CD-tied tri-phone models with 1, 2, 4 and 8 Gaussians per state, and are reported in the following tables. Case-1: Testing the wireline coded data (NB Codecs) with G.711-coded trained models The corresponding comparative analysis based on ASR accuracy with respect to G.711, G.726, G.729 and Un-coded are as shown in Figure-1. It is evident that the G.726 and G.711 have reported high accuracy coefficient. Table-3. Results of testing the wireline coded data (NB Codecs) with G.711-coded trained models (8kHzHMMs. Coded data used in testing Un-coded G G G

3 Figure-1. Graphic results of testing the wireline coded data (NB Codecs) with G.711-coded trained models (8kHzHMMs). Case-2: Testing of the coded data with G.729-coded models (8-kHz HMMs) is coded with all other wireline codecs, such as G.711, G.726 and G.729, is tested with the G.729-coded models (8-kHz HMMs) for the CI, and CD-tied tri-phone models with 1, 2, 4 and 8 Gaussians per state, and are reported. Results of testing the wireline coded data (NB codecs) with G.729-coded trained models (8kHzHMMs). Table-4. Testing of the coded data with G.729-coded models (8-kHz HMMs). Coded data used in testing Un-coded G G G It can be inferred from the comparative results shown in Figure.2 for G.711, G.726, G.729 and Un-coded for testing the wireline Codecs with 8 KHz G.729 coded HMMs, that the impact of the respective coding is minimal. Figure-2. Graphic results of testing the wireline coded data (NB codecs) with G.729-coded trained models (8kHzHMMs). Case-3: Testing of the coded data with HR-coded models (8-kHz HMMs) is coded with all other NB wireless codecs, such as FR, EFR, HR, and AMR, is tested with the HR-coded models (8-kHz HMMs) for the CI, and CD-tied tri-phone models with 1, 2, 4 and 8 Gaussians per state, and are reported. Results of testing the wireless coded data (NB Codecs) with HR-coded trained models (8kHzHMMs. Table-5. Testing of the coded data with HR-coded models (8-kHz HMMs). Coded data used in testing Un-coded FR EFR HR

4 when compared as shown in Figure-3. HR reported minimal, however almost similar to EFR for CD-8gau. Figure-3. Graphic results of testing the wireless coded data (NB Codecs) with HR-coded trained models (8kHzHMMs). FR reported to be having high accuracy coefficient when compared with EFR, HR and Un-coded Case-4: Testing of the coded data with AMR4.75-coded models (8-kHz HMMs) is coded with all other NB wireless codecs, such as FR, EFR, HR, and AMR, and wireline codecs, such as G.711, G.726 and G.729 are tested with the coded models (8-kHz HMMs) for the CI, and CD-tied triphone models with 1, 2, 4 and 8 Gaussians per state, and are reported. Results of testing the wireless coded data (NB Codecs) with AMR@4.75-coded trained models (8kHzHMMs) Table-6. Testing of the coded data with AMR4.75-coded models (8-kHz HMMs). Coded data used in testing Un-coded FR EFR HR AMR@ AMR@ G G G When compared with respect to accuracy as shown in Figure-4, it is clearly evident that the FR expressed high accuracy while Un-coaded produced poor results while testing the wireless coded data (NB Codecs) with AMR@4.75-coded trained models (8kHzHMMs). Figure-4. Graphic results of testing the wireless coded data (NB Codecs) with AMR@4.75-coded trained models (8kHzHMMs). Case-5: Testing of the AMR coded data with AMR12.2-coded models (8-kHz HMMs) is coded with all other NB wireless codecs, such as FR, EFR, HR, and AMR, is tested with the AMR@12.2kbps coded models for the CI, and CD-tied tri-phone models with 1, 2, 4 and 8 Gaussians per state, and are reported in the following table. 1389

5 Table-7. Results of testing the AMR coded data (NB codecs) with trained models (8kHzHMMs). Coded data used in testing Un-coded FR EFR HR models. The ASR performance is almost same when tested with either 16-kHz coded models or 8-kHz un-coded models. The ASR performance is poor when tested with 16-kHz un-coded models. REFERENCES [1] X. Huang, A. Acero and H. W. Hon Spoken Language Processing: A Guide to Theory, Algorithm and System Development, Prentice Hall. Figure-5. Graphic results of testing the wireless coded data (NB Codecs) with AMR@12.2-coded trained models (8kHzHMMs). While comparing during the testing the wireless coded data (NB Codecs) with AMR@12.2-coded trained models (8kHzHMMs) in connection with the previous analysis in Case-4, the respective FR produced accuracy at high degree with respect to EFR, HR, AMR@4.75 and AMR@12.2 also including the un-coded. 4. CONCLUSIONS ASR accuracies accuracy for un-coded and coded data when tested with the different coded models that include 8-kHz and 16-kHz HMMs. The major observations made are as follows. The ASR accuracy always increases with 8-kHz coded trained models when compared to 8-kHz un-coded models for all the narrowband codecs. The ASR accuracy of coded data of any particular codec increases by at least 2% when the same type of coded models is used. The ASR results for coded data for specific codecs, such as G.711, G.729, HR and AMR12.2 for un-coded and respective coded models, are re-organized to see the ASR improvements. Coded data (G.711/G.729/HR/AMR12.2) tested with 8-kHz uncoded HMMs while the Coded data (G.711/G.729/HR/AMR12.2) tested with 16-kHz uncoded HMMs. Similarly, the Coded data (G.711/G.729/HR/AMR12.2) tested with 8-kHz Coded (G.711/G.729/HR/AMR12.2) HMMs whereas the Coded data (G.711/G.729/HR/AMR12.2) tested with 16-kHz Coded (G.711/G.729/HR/AMR12.2) HMMs. All these codecs perform well for the respective 8-kHz coded [2] K. W. Church and R. L. Mercer Introduction to the Special Issue on Computational Linguistics Using Large Corpora. Computational Linguistics. 19(1): [3] Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Advanced Front-End Feature Extraction Algorithm; Compression Algorithms. ETSI. [4] J. S. Garofolo et al TIMIT Acoustic-Phonetic Continuous Speech Corpus. Linguistic Data Consortium, Philadelphia, USA. [5] J. S. Garofolo et al FFMTIMIT. Linguistic Data Consortium, Philadelphia, USA. [6] 3GPP Mandatory Speech Codec Speech Processing Functions: AMR Speech Codec; Transcoding Functions (3G TS ). [7] C. Jankowski et al NTIMIT: A Phonetically Balanced, Continuous Speech, Telephone Bandwidth Speech Database. In Proc. of ICASSP, pp [8] K.-F. Lee and H.-W. Hon Speaker-Independent Phone Recognition Using Hidden Markov Models. IEEE Transactions on Acoustics, Speech and Signal Processing. 37(11):

6 [9] N. Morales et al STC-TIMIT: Generation of a Single-channel Telephone Corpus. In Proc. of LREC. pp [10] D. A. Reynolds HTIMIT and LLHDB: Speech Corpora for the Study of Handset Transducer Effects. In Proc. of ICASSP. 2: [11] P. Bauer and T. Fingscheidt WTIMIT 1.0. Linguistic Data Consortium, Philadelphia. 1391