JP2014010688A - 入力文字推定装置およびプログラム - Google Patents
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Abstract
【解決手段】タッチパネルディスプレイに対してユーザが接触したときの入力座標を時系列に沿って記憶する時系列入力座標格納部3と、ソフトウェアキーボードの操作ボタンに割り当てられている文字毎に、複数種類の操作状態の文字モデルを定義し、各状態について2次元座標を特徴量として確率密度関数を定義し、各状態について定義した確率密度関数を文字毎に格納する文字モデル格納部4と、入力座標および文字モデルに基づいて、各文字の生起確率を演算する確率演算部5と、ユーザの入力開始からの確率値を累積した累積確率値を、各文字に対する累積生起確率として記憶する累積確率記憶部6と、累積生起確率が最も高い文字を入力候補文字として選出する入力候補文字選出部7と、を備える。
【選択図】図1
Description
pwi,l =Pwi (1)(x1,y1)* Qwi(1,1)と表わされる。また、L−R遷移(1から2)として、
*Pwi (1)(x2,y2)* Qwi(1,2)と表わされる。同様に、
*Pwi (2)(x3,y3)* Qwi(2,2)、
*Pwi (2)(x4,y4)* Qwi(2,2)、
*Pwi (2)(x5,y5)* Qwi(2,2)、
*Pwi (2)(x6,y6)* Qwi(2,3)、
*Pwi (3)(x7,y7)、と表わされる。
確率モデルを予め定義するだけでなく、あるタイミングにおいて、ユーザの入力座標と単語候補表示部から選択された単語履歴から再学習する機能を有する。例えば、予め集められたデータから学習する場合はユーザのタッチ座標時系列と、ユーザが意図と一致した文字にラベル付けされたデータ群からBaum-Welch reestimation algorithmなどの最尤学習を元にモデルを学習する。また、ユーザに適応した再学習を行なう場合は、デリートなどで消去されずに入力された文字と、タッチ座標時系列情報を結びつけて記憶しておき、端末がスリープになったときなどに再学習する機能を有する。この場合計算手法として、MAP適応などが挙げられる。
入力座標時系列だけでなく、入力座標時系列と時間から算出される速度を用いてもよい。
文字入力において、同一文字の入力でも、前のキーの位置や離した位置によりタッチ座標分布は変わりうる。そこで、図6に示すように、前のキーの位置もしくは、ソフトキーボード入力領域を含むタッチパネル上をn分割し、離した位置により同一文字に対してn個のモデルを持ってもよい。
文字入力において、同一文字の入力でも、端末の持ち方や姿勢によってもタッチ座標分布は変わりうる。そこで、n個のコンテクストを定義し、同一文字に対してn個のモデルを持ってもよい。もしくは、特徴量として、加速度、ジャイロ、磁力センサ等のパラメタを含めて確率モデルを定義してもよい。
確率モデルと確定的なキー入力を組み合わせてもよい。例えば、図7に示すようにあるキーの入力に対して上下左右4方向に文字が定義されている場合、方向毎に確定的に文字が定まる範囲が定義されており(図7:斜線部)、その範囲外については確率モデルを用いて文字を推定する。組み合わせとしては、以下の3組が考えられる。
・キーの位置が確率モデルおよびスライド方向が確率モデルと確定的方法の組み合わせ。
・キーの位置が確定的およびスライド方向が確率モデルと確定的方法の組み合わせ。
・キーの位置もスライド方向の両者とも確率モデルと確定的方法の組み合わせ。
ある押下位置から遠い位置のキーは、ユーザによる誤入力は起こりにくい。そこで、図8に示すように、ある閾値内に関する文字のみ、確率演算の対象とすることで、文字探索の高速化を行なっても良い。
確率計算された文字の上位N個の確率を保持しておいて、文字M個分の文字列について確率を、積算し、上位S個を入力文字列として表示してもよい。また、上位S個に対して、文字の前方一致などにより、辞書の予測機能を適応してもよい。その際、Sが多くなると端末上の予測単語表示部に表示しきれなくなりうる。そのため、図9に示すように、辞書の予測機能を適用するS個の入力文字列に対して、積算確率に応じてバイアスをかけ、積算確率が高い文字列ほど、予測単語が多く表示してもよい。また、確率言語モデルN-gramを導入して順位付けしてもよい。
モデル数が多くなると計算時間が多くなり得る。そこで、最初のタッチ位置に応じて共通のモデル化を行ない、モデルを“木構造化”してもよい。例えば、図10に示すように、一般にフリック入力では「あ」「い」「う」「え」「お」は、最初は「あ」を押してから異なる方向にスライドするため、その「あ」〜「お」の状態1を共通のモデル化するということが挙げられる。同様に、上記2種のコンテクストについても、同一文字についてある状態のみ共通のモデル化してもよい。その際、どのモデル・状態を共通モデル化するかは手動で決めても、あるクラスタリング手法を用いてもよい。
ある文字または文字列を入力したあとに、デリートにより消去した場合、その文字はユーザの意図と異なる入力がされた可能性がある。そのため、さらなる誤入力を防ぐためにデリートした文字にバイアス(<1)をかけてもよい。なお、図11に示すように、バイアスをかける対象文字は直前にデリートしたDBの順番を含む履歴に基づき判別する。すなわち、「ひらめ」と入力された後、「め」がデリートされた場合は、「め」の生起確率にバイアスをかける。また、「ら」がデリートされた場合は、「ら」の生起確率にバイアスをかける。
文字モデル部および演算部、記憶部をサーバが有し、本装置への入力座標をサーバに送信することで、確率演算を行なっても良い。その場合、サーバで確率演算した結果を本装置が受信し、表示部にて表示する。
2 タッチパネル入力部
3 時系列入力座標格納部
4 文字モデル格納部
5 確率演算部
6 累積確率記憶部
7 入力候補文字選出部
8 文字表示部
Claims (10)
- タッチパネルディスプレイに表示されているスライド操作により文字入力を行なうソフトウェアキーボードに対するユーザの操作を検出し、ユーザが入力する文字を推定する入力文字推定装置であって、
前記タッチパネルディスプレイに対してユーザが接触したときの入力座標を時系列に沿って記憶する時系列入力座標格納部と、
前記ソフトウェアキーボードの操作ボタンに割り当てられている文字毎に、複数種類の状態を持つ確率文字モデルを定義し、各状態について2次元座標から得られる特徴量空間における確率密度関数を定義し、前記各状態について定義した確率密度関数を文字毎に格納する文字モデル格納部と、
前記入力座標および前記文字モデルに基づいて、入力座標に対して各文字モデル毎に生起確率を演算する確率演算部と、
各文字モデル毎にユーザの入力開始からの前記確率値を累積した累積確率値を、各文字に対する累積生起確率として記憶する累積確率記憶部と、
前記累積生起確率が最も高い文字を入力候補文字として選出する入力候補文字選出部と、を備えることを特徴とする入力文字推定装置。 - 前記2次元座標に加えて、前記2次元座標から算出される速度ベクトルまたは加速度ベクトルまたは時系列入力座標から算出されるその他の値を前記特徴量として用いることを特徴とする請求項1記載の入力文字推定装置。
- 過去に入力された文字およびその入力座標の履歴を記録する入力履歴管理部と、
文字モデルを再学習する文字モデル再学習部と、を更に備え、
前記再学習部は、過去に入力された文字毎の確率密度関数に対して、その文字を入力したときの接触点の2次元座標またはその接触点を含む一定領域の確率値を高くすることを特徴とする請求項1または請求項2記載の入力文字推定装置。 - 前記ソフトウェアキーボードの入力領域を含む前記タッチパネルディスプレイを複数の領域に分割し、ユーザの直前の入力座標がどの領域に含まれたかに応じて定まる文字モデルを文字毎に定義することを特徴とする請求項1から請求項3のいずれかに記載の入力文字推定装置。
- ユーザの操作姿勢に応じて定まる文字モデルを文字毎に定義することを特徴とする請求項1から請求項3のいずれかに記載の入力文字推定装置。
- ユーザが押した座標と離した座標とに基づいて文字が確定的に定まる確定的文字決定領域外に対して、前記文字モデルを用いることを特徴とする請求項1から請求項3のいずれかに記載の入力文字推定装置。
- 押下位置を共通に割り当てられた文字に共通のモデル化を行なって、前記文字モデルを木構造化することを特徴とする請求項1から請求項3のいずれかに記載の入力文字推定装置。
- ユーザが文字入力後に削除した場合、その削除された文字に対して確率値を小さくすることを特徴とする請求項1から請求項3のいずれかに記載の入力文字推定装置。
- 前記入力候補文字選出部は、確率計算された文字の上位N個の確率を保持し、文字M個分の文字列について確率を積算し、上位S個を入力文字列として選出することを特徴とする請求項1から請求項8のいずれかに記載の入力文字推定装置。
- タッチパネルディスプレイに表示されているスライド操作により文字入力を行なうソフトウェアキーボードに対するユーザの操作を検出し、ユーザが入力する文字を推定するプログラムであって、
前記タッチパネルディスプレイに対してユーザが接触したときの入力座標を時系列に沿って記憶する処理と、
前記ソフトウェアキーボードの操作ボタンに割り当てられている文字毎に、複数種類の状態を持つ確率文字モデルを定義し、各状態について2次元座標から得られる特徴量空間における確率密度関数を定義し、前記各状態について定義した確率密度関数を文字毎に格納する処理と、
前記入力座標および前記文字モデルに基づいて、入力座標に対して各文字モデル毎に生起確率を演算する処理と、
各文字モデル毎にユーザの入力開始からの前記確率値を累積した累積確率値を、各文字に対する累積生起確率として記憶する処理と、
前記累積生起確率が最も高い文字を入力候補文字として選出する処理と、の一連の処理を、コンピュータに実行させることを特徴とするプログラム。
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