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我正在开发一个应用程序来使用语音识别在 Android 设备上收集现场数据。有五个“目标词”，以及几个被识别的数字（零、一、十、一百等）。

我通过添加同音词（同音词）以及白话同义词提高了目标词的准确性。目标词是 Chinook、sockeye、coho、pink 和 chum。这是相关代码，

 public void parseWords() {
    List<String> szlNumbers = Arrays.asList(new String[]{"ONE", "TEN", "ONE HUNDRED", "ONE THOUSAND", "TEN THOUSAND"});
    //species with phonemes and vernacular names
    List<String> szlChinook = Arrays.asList("CHINOOK", "CHINOOK SALMON", "KING", "KINGS", "KING SALMON", "KING SALMAN");
    List<String> szlSockeye = Arrays.asList("SOCKEYE", "SOCCER", "SOCKEYE SALMON", "SOCK ICE", "SOCCER ICE", "SOCK I SAID", "SOCCER IS", "OKAY SALMON", "RED SALMON", "READ SALMON", "RED", "REDS");
    List<String> szlCoho = Arrays.asList("COHO", "COHO SALMON", "COVER SALMON", "SILVER SALMON", "SILVER", "SILVERS", "CO", "KOBO", "GO HOME", "COMO", "COVER", "GO");
    List<String> szlPink = Arrays.asList("PINK", "A PINK", "PINKS", "PINK SALMON", "HANK SALMON", "EXAMINE", "HUMPY", "HOBBY", "HUMPIES", "HUM BE", "HUM P", "BE", "HUMPTY", "HOBBIES", "HUMVEE", "THE HUMVEES", "POMPEY");
    List<String> szlChum = Arrays.asList("CHUM", "JOHN", "JUMP", "SHARMA", "CHARM", "COME", "CHARM SALMON", "COME SALMON", "CHUM SALMON", "JUMP SALMON", "TRUMP SALMON", "KETA SALMON", "KETA", "DOG", "DOGS", "DOG SALMON", "GATOR", "GATORS", "CALICO", "A CALICO");

    //Collections.sort(szlChinook); //what is this?
    szVoskOutput=szVoskOutput.toUpperCase();

    if (szVoskOutput.compareTo("")==0){
        //do nothing, this is a blank string
        return;
    }
    if(szVoskOutput==null){//...and this is a null string
        return;
    }
    //pink
    if (szlPink.contains(szVoskOutput)) {
        szSpecies = "Pink";
        populateSpecies();
        return;
    }
    //chum
    if (szlChum.contains(szVoskOutput)) {
        szSpecies = "Chum";
        populateSpecies();
        return;
    }
    //sockeye
    if (szlSockeye.contains(szVoskOutput)) {
        szSpecies = "Sockeye";
        populateSpecies();
        return;
    }
    //coho
    if (szlCoho.contains(szVoskOutput)) {
        szSpecies = "Coho";
        populateSpecies();
        return;
    }
    //Chinook
    if (szlChinook.contains(szVoskOutput)) {
        szSpecies = "Chinook";
        populateSpecies();
        return;
    }
    if(szlNumbers.contains(szVoskOutput)) {//then this is a number, put in count txt box
        tvCount.setText(szVoskOutput);
       return;
    }else{
            Toast.makeText(this, "Please repeat clearly. Captured string is:" + szVoskOutput, Toast.LENGTH_SHORT).show();
    }
}//end parseWords()

我在 GitHub 上有一个带有源代码的应用程序的简化版本：https ://github.com/portsample/salmonTalkerLite 以及 Google Play 上的最新完整版本：https: //play.google.com/store/apps/详情？id=net.blepsias.salmontalker

使用目标词和同音词，我可以在四到五秒内获得成功。我想让这更快。我可以做些什么来进一步调整速度？

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1 回答 1

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这有很大帮助。识别时间现在始终保持在 1.5 秒左右。

  private void recognizeMicrophone() {
    if (speechService != null) {
        setUiState(iSTATE_DONE);
        speechService.stop();
        speechService = null;
    } else {
        setUiState(iSTATE_MIC);
        try {
        Recognizer rec = new Recognizer(model, 16000.f, "[\"sockeye pink coho chum chinook atlantic salmon\","[unk]"]");
            speechService = new SpeechService(rec, 16000.0f);
            speechService.startListening(this);
        } catch (IOException e) {
            setErrorState(e.getMessage());
        }
    }
}

这会清除上游无关的 Vosk 输出，只留下指定的目标词。这将消除对原始帖子中显示的复杂同音词排序条件的需要。感谢 Nickolay Shmyrev 为此。我仍在寻找其他方法来加快识别速度，或以其他方式改进此过程。

更新和改进将反映在 GitHub 上的源代码中：https ://github.com/portsample/salmonTalkerLite

于 2022-01-14T21:36:03.483 回答