Description:In recent years, the adoption of voice-based technologies has seen exponential growth, enhancing the accessibility of various applications. This research article presents a novel approach to developing a voice-based email system using Support Vector Machine (SVM) algorithms. The proposed system aims to bridge the communication gap and improve accessibility for individuals with visual impairments or limited dexterity, facilitating seamless email communication through voice interactions. We detail the system architecture, data preprocessing, feature extraction techniques, and the SVM-based classification model used for speech-to-text conversion. The experimental results demonstrate the effectiveness of the SVM algorithms, yielding high accuracy and efficiency in transcribing voice inputs into text, thereby enabling a more inclusive and user-friendly email experience.
5 Claims & 1 Figure , Claims:The scope of the invention is defined by the following claims:

Claim:
1. A system/method for automated voice based SVM algorithms, said system/method comprising the steps of:
a) The system initiates with voice input processing (1), from this data feature extraction will be performed (2).
b) After that, the SVM based speech recognition (3) is started with SVM model (4) training to convert the speech to text (5).
c) The email composition and delivery will work (6) to send the mail based on the user voice.
d) The system continuously collects the feedback (7) from the user and improves the learning habits of the SVM model.
2. As mentioned in claim 1, the audio signal undergoes preprocessing to remove noise, normalize speech rates, and segment speech utterances into smaller units for better recognition.
3. According to claim 1, the extracted features are used as input to an SVM-based classification model. The SVM algorithm is a machine learning technique used for binary and multiclass classification tasks. In the context of the Voice-Based Email System, the SVM model is trained to recognize different phonetic units and map them to corresponding text characters or words.
4. As per claim 1, to train the SVM model, a labeled dataset of speech samples and their corresponding text transcriptions is required. The dataset consists of pairs of input features (MFCCs, pitch, etc.) and their corresponding target labels (text characters or words). The SVM algorithm optimizes its parameters and hyperplane during the training process to achieve the best possible accuracy in recognizing the phonetic units.
5. As per claim 1, once the voice input is converted into text, the system proceeds to compose the email message based on the recognized text. The email message can be further edited or reviewed by the user before sending it to the recipient.
6. As per claim 1, the system can prompt users to review and rate the accuracy of transcribed text in their emails. This feedback is valuable for improving the SVM model through retraining or fine-tuning, thus enhancing the system's overall performance.