بهبود سرعت و دقت در استفاده از برنامه‌نويسي ژنتيک براي تصديق هويت گوينده
محورهای موضوعی : مهندسی برق و کامپیوترسعيده سادات سديدپور 1 , محمدمهدی همایون‌پور 2 , مهدي فسنقري 3
1 - دانشگاه صنعتي اميرکبير
2 - دانشگاه صنعتی امیرکبیر
3 - پژوهشکده فناوري اطلاعات
کلید واژه: گوينده تصديق هويت گوينده برنامه‌نويسي ژنتيک خوشه‌بندي ويژگی MFCC ويژگی PLP,
چکیده مقاله :
در تصديق هويت گوينده، سيستم هويت شخصي را که با سيستم تماس برقرار کرده است، بررسي کرده و تعيين مي‌کند که وي همان شخص مدعي است و يا دروغ مي‌گويد. در اين مقاله از برنامه‌نويسي ژنتيک به‌عنوان روشی برای مدل‌سازي گويندگان استفاده شد. با توجه به زمان زياد آموزش مدل‌ها توسط برنامه‌نويسي ژنتيک، ايده بهره‌مندي از فشرده‌سازي داده‌هاي آموزشي، بهمنظور کاهش زمان آموزش مدل‌ها مطرح گرديد و بدين ترتيب زمان لازم برای مدل‌سازی گويندگان با استفاده از برنامه‌نويسي ژنتيک در حدود 20 برابر کاهش داده شد. آموزش چندين درخت برنامه‌نويسي ژنتيک به‌عنوان مدل هر گوينده، ايده ديگري است که بهمنظور بهبود دقت تصديق هويت گوينده در اين مقاله مطرح شده است. در اين روش، داده‌هاي آموزشي به تعداد کمي خوشه تفکيک شده و به‌ازاي هر خوشه، يک درخت برنامه‌نويسي ژنتيک آموزش داده مي‌شود. بدين ترتيب يک گوينده با چندين درخت برنامه‌نويسي ژنتيک مدل مي‌شود. با استفاده از روش پيشنهادي، کارايي برنامه‌نويسي ژنتيک براي تصديق هويت گوينده از 50% به حدود 92% افزايش پيدا کرده است. نتايج حاصل از عملکرد برنامه‌نويسي ژنتيک با کارايي روش‌هاي تمايزي ديگري مثل شبکه‌های عصبي MLP و LVQ و نيز روش‌هاي غير تمايزي مانند LBG، GMM، GMM-UBM و VQ-MAP مقايسه گرديد و مشاهده شد که برنامه‌نويسي ژنتيک کارايي بهتري را نسبت به ديگر روش‌ها نتيجه مي‌دهد.
In speaker verification, a system investigates a person's identity and decides whether the person is a true client or an imposter. In this paper, genetic programming (GP) is used as a method for speaker modeling. When GP is used for construction of models for speakers, due to long training time to train GP models, training data compression is proposed in this paper. This idea reduced training time for 20 times. Training of several GP trees as a speaker's model is another idea presented in this paper to improve the speaker verification performance. In this method, training data are separated to a few clusters. Then a GP tree is trained for each cluster. Therefore, a speaker is modeled by several genetic programming trees. The verification performance increased from 50% to about 92% using the proposed method. Genetic programming performance was compared to some other discriminative methods such as Multi-Layer Perceptron neural network and Learning Vector quantization, and generative methods such as K-Means, GMM and LBG, GMM-UBM and VQ-MAP. Experiments show that Genetic programming is more effective than the other methods.
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