Select the appropriate AI voice recognition service and integrate the PHP SDK; 2. Call ffmpeg with PHP to convert the recording into the API-required format (such as wav); 3. Upload files to cloud storage and call API asynchronous recognition; 4. Analyze JSON results and organize text using NLP technology; 5. Generate Word or Markdown documents to complete the automation of meeting records. The entire process needs to ensure data encryption, access control and compliance to ensure privacy and security.

PHP integrates AI voice recognition and transliteration to automatically generate conference records. The core lies in using the API provided by existing AI voice recognition services (such as Alibaba Cloud, Tencent Cloud, Baidu Cloud, etc.), combining PHP's powerful back-end processing capabilities to complete the voice-to-text conversion, and perform preliminary sorting, and finally generate editable conference records.

Solution

1. Choose the right AI voice recognition service: Choose the right voice recognition service provider based on actual needs (such as recognition accuracy, supported language, price, etc.). Most service providers provide PHP SDK or API for easy integration.

   

2. Recording file processing: Conference recordings are usually in various formats (such as mp3, wav). The recording file needs to be uploaded to the server and may need to be formatted to meet the requirements of the speech recognition API. You can use PHP's ffmpeg extension for format conversion.

    <?php
   $inputFile = &#39;/path/to/your/meeting.mp3&#39;;
   $outputFile = &#39;/path/to/your/meeting.wav&#39;;
   $command = &#39;/usr/bin/ffmpeg -i &#39; . $inputFile . &#39; -acodec pcm_s16le -ac 1 -ar 16000 &#39; . $outputFile;
   exec($command, $output, $return_var);
   
   if ($return_var !== 0) {
       echo "Error converting file: " . implode("\n", $output);
   } else {
       echo "File converted successfully!";
   }
   ?>

   Note: ffmpeg is required to be installed and ensure that PHP has execution permissions.

   

3. Call the speech recognition API: Use the PHP SDK or API of the selected speech recognition service to send the recording file to the server for recognition. This usually involves steps such as authentication, file upload, parameter setting, etc.

    <?php
   // Suppose you use Alibaba Cloud&#39;s voice recognition API
   require_once &#39;aliyun-openapi-php-sdk/aliyun-php-sdk-core/Config.php&#39;;
   use Aliyun\Core\Config;
   use Aliyun\Core\Profile\DefaultProfile;
   use Aliyun\Core\DefaultAcsClient;
   use Aliyun\SpeechRecognizer\Request\V20160223 as SR;
   
   Config::load();
   $iClientProfile = DefaultProfile::getProfile("cn-shanghai", "<your_access_key_id>", "<your_access_key_secret>");
   DefaultProfile::addEndpoint("cn-shanghai", "cn-shanghai", "nls-filetrans.cn-shanghai.aliyuncs.com", "nls-filetrans");
   $client = new DefaultAcsClient($iClientProfile);
   
   $request = new SR\SubmitFileTransRequest();
   $request->setFormat("wav");
   $request->setSampleRate(16000);
   $request->setEnableWords("true");
   $request->setFileLink("http://your-oss-bucket.oss-cn-shanghai.aliyuncs.com/meeting.wav"); // The link uploaded to OSS $request->setUserId("your_user_id");
   
   $response = $client->getAcsResponse($request);
   print_r($response);
   
   // Is it necessary to poll and identify the results in the future?>

   Note: You need to upload meeting.wav to cloud storage services such as OSS and obtain links that can be accessed by the public network. You need to replace <your_access_key_id> , <your_access_key_secret> , your_user_id and other information in the sample code.

4. Processing recognition results: The speech recognition service returns text results in JSON format. You need to parse JSON and extract text content.

5. Meeting minutes sorting: Preliminary sorting of the extracted text content, such as adding timestamps, distinguishing spokespersons, etc. This part can be combined with natural language processing (NLP) technology, such as using PHP's TextRazor or MonkeyLearn libraries to perform keyword extraction, sentiment analysis, etc. to improve the quality of meeting minutes.

6. Generate editable documents: Generate organized text content to editable documents, such as Word documents or Markdown documents. You can use PHPWord or Parsedown libraries for PHP.

How to improve the accuracy of PHP voice recognition to text?

1. Optimize recording quality: High-quality recording is the basis for improving recognition accuracy. Use professional recording equipment to reduce noise interference and ensure clear voice.

2. Choose the right voice recognition engine: Different voice recognition engines perform differently in different scenarios. You can try using multiple engines and choose the one that suits your scenario best.

3. Use a customized voice model: If the content of the conference involves a specific domain term, consider using a customized voice model. Some voice recognition service providers provide customized services that can train models based on corpus in specific fields to improve recognition accuracy.

4. Post-processing optimization: Post-processing of recognition results, such as correcting spelling errors, adding punctuation marks, adjusting word order, etc. Simple post-processing can be performed using PHP's string processing functions and regular expressions. You can also use more advanced NLP technologies, such as using PHP's OpenCC library for simplified and traditional Chinese conversion, and using the Pinyin library to convert pinyin into Chinese characters.

5. Add context information: When calling the speech recognition API, some context information can be provided, such as conference topics, participants, etc. This can help the voice recognition engine better understand the voice content and improve the recognition accuracy.

How to solve the problem of long audio processing in PHP voice recognition?

1. Sharding processing: Split long audio files into multiple small segments for speech recognition respectively. This can avoid memory overflow or timeout processing large amounts of data at once. Audio segmentation can be performed using PHP's ffmpeg extension.

    <?php
   $inputFile = &#39;/path/to/your/long_audio.mp3&#39;;
   $segmentDuration = 60; // Duration of each segment, unit: seconds $outputDir = &#39;/path/to/your/segments/&#39;;
   
   $command = &#39;/usr/bin/ffmpeg -i &#39; . $inputFile . &#39; -f segment -segment_time &#39; . $segmentDuration . &#39; -c copy &#39; . $outputDir . &#39;segment_ d.mp3&#39;;
   exec($command, $output, $return_var);
   
   if ($return_var !== 0) {
       echo "Error splitting file: " . implode("\n", $output);
   } else {
       echo "File split successfully!";
   }
   ?>

   Note: ffmpeg is required to be installed and ensure that PHP has execution permissions.

2. Asynchronous processing: Put the voice recognition task into the queue and process it asynchronously. This can avoid blocking the main thread and improve the system's response speed. Message queue services such as RabbitMQ or Redis can be used for PHP.

3. Using streaming voice recognition: Some voice recognition service providers provide streaming voice recognition APIs, which can receive audio data in real time and identify them. This can reduce latency and improve user experience.

4. Optimize server configuration: Long audio processing requires a lot of computing resources. You can consider upgrading the server configuration, such as increasing memory, CPU, etc.

5. Using cloud functions or Serverless services: Deploy voice recognition tasks on cloud functions or Serverless services, you can use the elastic scaling capabilities of the cloud platform to automatically allocate computing resources and improve processing efficiency.

How to protect the privacy and security of meeting minutes?

1. Data encryption: Encrypt the recording files and identification results. Encryption can be used with PHP's openssl extension.

2. Access Control: Restrict access to meeting minutes. Only authorized personnel can access the minutes. You can use PHP's permission management system, such as RBAC (Role-Based Access Control).

3. Data desensitization: Desensitize sensitive information in meeting minutes, such as name, phone number, ID number, etc. Data desensitization can be performed using PHP regular expressions.

4. Secure transmission: Use HTTPS protocol to transmit data to prevent data from being eavesdropped.

5. Regular audit: Regular audit of the visits and modifications of meeting minutes to promptly discover and deal with security issues.

6. Compliance: Ensure that the entire process complies with relevant laws and regulations, especially with regard to data privacy protection, such as GDPR.

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