在本快速入門導覽課程中，您將瞭解如何評估及提升音訊資料的 Google Cloud 語音轉文字準確率。此外，您也可以瞭解 API 提供的各種模型和選項，進一步提升轉錄準確度。瞭解如何使用 Google Cloud 控制台中的 Speech-to-Text 使用者介面和真值檔案，評估準確率並深入瞭解 Speech-to-Text 系統。
機器學習 (ML) 系統本質上容易出錯，自動語音辨識 (ASR) 系統 (又稱語音轉文字系統) 也不例外。準確度評估結果與特定用途和評估系統密切相關，因為錄音品質和聲學條件的差異可能會大幅影響準確度。因此，為所有客戶和用途提供單一準確度分數並不實際。為確保 ASR 系統在關鍵的正式環境系統中穩定運作，此外，瞭解語音轉文字在系統整體環境中的效能也很重要。
在本快速入門指南中，請使用業界標準的比較方法「字詞錯誤率 (WER)」(通常簡稱為 WER)。如要進一步瞭解 WER 的計算和解讀方式，請參閱「評估及提升語音準確度」。開始吧
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-11 (世界標準時間)。"],[],[],null,["# Measure and improve accuracy\n\nIn this quickstart, learn how to measure and improve the accuracy of the Google Cloud Speech-to-Text for your audio data. Also explore the various models and options available from the API to enhance transcription accuracy. Explore how to use the Speech-to-Text UI in the Google Cloud console and a ground-truth file to measure accuracy and to gain insights into the Speech-to-Text system.\n\nMachine Learning (ML) systems are inherently subject to inaccuracies, and Automatic Speech Recognition (ASR) systems, also known as Speech-to-Text systems, are no exception. Accurate measurement of accuracy is strongly coupled to specific use cases and the systems being evaluated, as differences in audio recording quality and acoustic conditions can significantly impact accuracy. As a result, a singular accuracy score for all customers and use cases is impractical. To ensure reliable performance of ASR systems in critical production-facing systems performance. It is also essential to understand how Speech-to-Text performs within the broader context of your system.\n\nFor the purposes of this quickstart guide,use the industry standard method for comparison, [Word Error Rate (WER)](https://en.wikipedia.org/wiki/Word_error_rate), often abbreviated as WER. For more information on how WER is calculated and interpreted see [Measure and improve speech accuracy](/speech-to-text/docs/speech-accuracy). Let's start.\n\nGetting started with Speech-to-Text Console\n-------------------------------------------\n\n#### Permissions required for this task\n\nTo perform this task, you must have the following\n[permissions](/iam/docs/overview#permissions):\n\n\n- `storage.buckets.get`\n- `storage.buckets.list`\n\nAt the project or bucket level:\n\n- `storage.objects.create`\n- `storage.objects.get`\n- `storage.objects.list`\n- `storage.objects.update`\n\nEnsure you have signed up for a Google Cloud account and created a project.\n1. Go to Speech in Google Cloud console, and navigate to [Speech-to-Text UI](https://console.cloud.google.com/speech).\n2. Using an audio file that is acoustically representative of your use case and how you are planning to use the ASR system, follow the quickstart instructions for making your first transcription using the [Speech-to-Text](https://cloud.google.com/speech-to-text/docs/transcribe-console).\n\nCalculating Transcription Accuracy\n----------------------------------\n\n1. After you have successfully transcribed your audio file, use the `Transcription Accuracy` section. This section remains empty until accuracy is calculated for your transcription.\n2. Using the **Upload Ground Truth** button at the top of the section, you can begin calculating accuracy.\n\nSpecifying ground truth\n-----------------------\n\n1. To calculate the accuracy of the transcription, provide a ground truth file. This is a `.txt` or `.csv` file, usually a human-generated transcription file that contains the correct or expected transcriptions for comparison.\n2. Using `gs://cloud-samples-data/speech/brooklyn_bridge.wav` as an example. The ground truth file contains: `How old is the Brooklyn Bridge`. If you don't have a ground truth file available, a recommendation is to download the transcription in a text format. Edit the transcription file as needed. Upload the transcription file as the ground truth file.\n3. Using **Upload** or an existing Cloud Storage file, specify the ground truth file, and click **Save** .\n\nConfirming ground truth\n-----------------------\n\n1. After clicking **Save**, a prompt displays to confirm that the specified ground truth file is correct. Verify that the ground truth file accurately represents the correct transcriptions, as it directly affects the accuracy metrics.\n2. Click **Confirm** to proceed.\n\nReview evaluation results\n-------------------------\n\n1. Depending on the size of the input data, the evaluation process might take some time, and the results are displayed upon completion.\n2. Once the evaluation is complete, the following sections are displayed:\n - The **Transcription Accuracy** table, the accuracy metrics, and a link to the ground truth file that were used in the process.\n - The `Transcription` with a toggle for comparing to the ground truth file along with a breakdown of accuracy metrics and highlights.\n3. Review and interpret the accuracy results to understand the performance of the Speech-to-Text recognizer that are used to identify areas for improvement, as the results vary depending on the inputs and transcription used. In the following examples, you can see indicative cases of the accuracy results, which provide valuable insights for optimization of the Google Cloud Speech-to-Text system.\n - An example of 0% WER:\n - An example of 40% WER:\n\nOptional: updating ground truth\n-------------------------------\n\nYou can test a different ground truth against the existing transcription, by reattaching a different file and then repeating steps three and four with an updated ground truth file.\n\nTry it for yourself\n-------------------\n\n\nIf you're new to Google Cloud, create an account to evaluate how\nSpeech-to-Text performs in real-world\nscenarios. New customers also get $300 in free credits to run, test, and\ndeploy workloads.\n[Try Speech-to-Text free](https://console.cloud.google.com/freetrial)"]]
